Two different bus stop layout designs : A traffic simulation study in Vissim

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(1)LiU-ITN-TEK-A--20/055-SE. Two different bus stop layout designs - A traffic simulation study in Vissim Mohamed Elsayed Erik Torstensson 2020-09-25. Department of Science and Technology Linköping University SE-601 74 Norrköping , Sw eden. Institutionen för teknik och naturvetenskap Linköpings universitet 601 74 Norrköping.

(2) LiU-ITN-TEK-A--20/055-SE. Two different bus stop layout designs - A traffic simulation study in Vissim The thesis work carried out in Transportsystem at Tekniska högskolan at Linköpings universitet. Mohamed Elsayed Erik Torstensson Norrköping 2020-09-25. Department of Science and Technology Linköping University SE-601 74 Norrköping , Sw eden. Institutionen för teknik och naturvetenskap Linköpings universitet 601 74 Norrköping.

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(4) Master of Science Thesis in Communications and Transport system Department of Science and Technology, Linköping University, 2020. Two different bus stop layout designs - A traffic simulation study in Vissim. Erik Torstensson & Mohamed Elsayed.

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(6) Master of Science Thesis Erik Torstensson and Mohamed Elsayed LiTH-ISY-EX--ET--YY/XXXX Supervisors: Ivan Postigo ITN, Linkoping University Mats Sandin M4Traffic Examiner: Johan Olstam ITN, Linkoping University. Communications and Transport system Department of Science and Technology Linköping University SE-581 83 Linköping, Sweden Copyright 2020 Erik Torstensson and Mohamed Elsayed.

(7) Abstract Bicycles are one of the most efficient way to travel within short-distance trips due to its relatively low maintenance and operation costs (Gao, Liu, & Feng, 2012). Besides to their efficiency, bicycles provide more flexibility for their parking and its ease of use. With the increasing transportation demand in major cities, transportation authorities will encourage to use the bicycle more. However, accommodating bicycles would require physical road geometry modifications such as introducing dedicated bicycle-lanes. Cycling appears to be a sustainable form of transportation across virous countries in Europe, as a result the authorities are planning and implementing upgrades to make the transportation system safer, convenient and sustainable which is necessary to encourage more people to use bicycles as a form of transportation. This thesis is a case study which examines current traffic conditions on a bus stop at Langholmsgatan in the city of Stockholm, Sweden and evaluates the effects of different designs for bicycles and buses. At this bus stop in Langholmsgatan, the bicycle lane is located to the right of the traffic road and to the left of the bus stop in the upstream direction. Buses need to cross the bicycle lane in order to arrive and departure the bus stop. Consequently, a conflict will also be created between bicycles and buses that are crossing the bicycle lane. In this thesis, an alternative design is evaluated in which buses and bicycles are separated from each other. However, this will result in a new conflict between bicycles and pedestrians. These two designs are evaluated in terms of travel time and delay and the analysis was done using micro-simulation software VISSIM. The study shows that the current design at the bus stop of Langholmsgatan should be preferred over the alternative design when considering travel time and delay for bicycles. If buses should be considered, the alternative design should be preferred over the current design.. Keywords: microscopic, simulation, VISSIM, driving behavior, travel time, delay..

(8) Acknowledgments Firstly, we would like to extend our deepest gratitude to our supervisors Ivan Postigo and Johan Olstam at Linkoping University for their support and feedback during this thesis. And we would also like to thanks M4traffic in Stockholm for giving us the opportunity to work with this thesis and especially our supervisors Mats Sandin and Anders Bernhardsson. Without your knowledge, full support and enthusiastic guidance we would never have been able to complete this thesis. We also would like to express our very profound gratitude to our families and friends for providing us with unfailing support and continuous encouragement throughout our years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. Thank you. We would like also to thank PTV for providing us with the VISSIM software license. Thank you all for making this possible.. Norrkoping, 2020 Erik Torstensson & Mohamed Elsayed. v.

(9) Table of content 1. Introduction ....................................................................................................................................... 11 1.1 Aim and research questions ................................................................................................... 3 1.2 Methodology ................................................................................................................................. 3 1.3 Study area ...................................................................................................................................... 4 1.4 Research Limitations and assumptions ............................................................................ 6 1.5 Structure of the report.............................................................................................................. 6 2. Bus stop layout guidelines .............................................................................................................. 8 3. Microscopic traffic simulation .................................................................................................... 11 3.1 Activities in a microscopic traffic simulation.............................................................. 12 3.2 Microscopic traffic simulation in Vissim........................................................................ 13 3.2.1 Car-following model in Vissim .................................................................................. 13 3.2.2 Lane changing .................................................................................................................. 15 3.2.3 Parameters of bicycles in Vissim.............................................................................. 16 3.2.4 Pedestrian behaviour modelling in Vissim.......................................................... 17 3.2.4.1 Pedestrian routing decisions ............................................................................ 19 3.2.5 Conflict modelling in Vissim ...................................................................................... 20 4. Data collection................................................................................................................................... 23 4.1 Speed and flow.......................................................................................................................... 24 4.2 Traffic signals ............................................................................................................................ 27 5. Simulation model development ................................................................................................. 29 5.1 Design A ....................................................................................................................................... 29 5.1.1 Bus Time Scheduling..................................................................................................... 32 5.1.2 Traffic Signals Modelling ............................................................................................. 33 5.1.3 Speed Modelling .............................................................................................................. 34 5.1.4 Vehicle Input ..................................................................................................................... 35 5.2 Design B ....................................................................................................................................... 35 5.4 Model Calibration .................................................................................................................... 38 5.4.1 Number of replications ................................................................................................ 39 vi.

(10) 5.4.2 Motorized vehicles ......................................................................................................... 40 5.4.3 Bicycles ............................................................................................................................... 42 6. Results .................................................................................................................................................. 52 6.1 Current traffic demand .......................................................................................................... 52 6.1.1 Travel time......................................................................................................................... 53 6.1.2 Delay .................................................................................................................................... 55 6.2 Different bicycle flows and bus frequencies................................................................. 56 6.2.1 Travel time......................................................................................................................... 56 6.2.2 Delay .................................................................................................................................... 58 7. Discussion & Conclusion ............................................................................................................... 59 7.1 Discussion ................................................................................................................................... 59 7.2 Conclusion .................................................................................................................................. 62 Bibliography............................................................................................................................................ 64 Appendix .................................................................................................................................................. 66 Appendix A ......................................................................................................................................... 67. vii.

(11) List of Figures Figure 1 – Bicycle lane on the left side of the bus stop in the upstream direction (Trafikverket, 2020 A). .................................................................................................................................... 2 Figure 2 – Bicycle lane on the right side of the bus stop in the upstream direction (Trafikverket, 2020 A)............................................................................................................... 3 Figure 3 – Plan view of the study case (Långholmsgatan). ...................................................... 5 Figure 4 – Sideview of the study case (Långholmsgatan). ....................................................... 5 Figure 5 – Bike lane between the bus stop and traffic lane (Trafikverket, 2020 A). ................. 9 Figure 6 – Recommended bus stop for a higher number of passengers (Trafikverket, 2020 A). ............................................................................................................................................... 10 Figure 7 – Building a simulation model steps (Olstam, 2019) ................................................ 12 Figure 8 – Wiedemann 99 model (PTV AG, 2018). ................................................................. 14 Figure 9 – Social force model (Dimakis & Guðmundsdóttir, 2018). ....................................... 18 Figure 10 – An illustration of two different lambda values in the Social force model (Dimakis & Guðmundsdóttir, 2018). ..................................................................................................... 19 Figure 11 – Example of a partial pedestrian routing (PTV AG, 2018).................................... 20 Figure 12 – Theoretical framework for the priority rule (PTV AG, 2018) ............................... 21 Figure 13 – Example of a roundabout with the use of the priority rule (PTV AG, 2018). ....... 22 Figure 14 – Shows the bus location & the location of video recording position. ................... 23 Figure 15 – Single vehicle data measurements (Treiber & Kesting, 2013). ............................ 24 Figure 16 – Shows the passing points of the speed estimation and distance between them visualized as an arrow. ........................................................................................................... 25 Figure 17 – The cumulative distribution curve CDF for motorized traffic. ............................. 26 Figure 18 – The cumulative distribution curve CDF for bicycles. ............................................ 26 Figure 19 – Shows the location of three traffic signals. ......................................................... 28 Figure 20 – Vissim for Design A.............................................................................................. 30 Figure 21 – 3D view for Design A. .......................................................................................... 30 Figure 22 – Shows the priority rules location and the conflict between the bikes and the entering buses from the bus stop. .......................................................................................... 32 Figure 23 – Shows standard bus size...................................................................................... 33 Figure 24 – Shows articulated bus size. ................................................................................. 33 Figure 25 – Shows the signal group for signal 1. ................................................................... 33 Figure 26 – Shows the signal group for signal 2 and 3. ......................................................... 33 Figure 27 – Desired speed distribution for motorized vehicles. ............................................. 34 Figure 28 – Desired speed distribution for Bicycles. ............................................................... 34 Figure 29 – Shows a 3D view for the Vissim scenario-2 model. ............................................. 35 Figure 30 – The conflict between pedestrians and bicyclists in Design B. .............................. 36 Figure 31 – Shows the first crosswalk of design B. ................................................................ 37 Figure 32 – Shows the second crosswalk of Design B. ........................................................... 38 Figure 33 – Speed estimation in Vissim.................................................................................. 39 Figure 34 – Confidence intervals for motorized vehicles on a 95% level. ............................... 41. viii.

(12) Figure 35 – Confidence interval for bicycles on a 95% level with default settings from COWI. ............................................................................................................................................... 43 Figure 36 – Confidence interval for bicycles on a 95% level with adjusted driving behaviour parameters. ........................................................................................................................... 45 Figure 37 – Desired Acceleration, COWI settings. .................................................................. 46 Figure 38 – Desired deceleration, COWI settings. .................................................................. 46 Figure 39 – Desired acceleration, changes. ........................................................................... 46 Figure 40 – Desired deceleration, changes. ........................................................................... 46 Figure 41 – Confidence interval for bicycles on a 95% level with changes in desired acceleration and deceleration. .............................................................................................. 47 Figure 42 – Speed reduction near the entry of bus stop and near the second traffic signal. . 48 Figure 43 – Speed reduction zones near the entry of bus stop after the modification. ......... 48 Figure 44 – Speed reduction at the first traffic signal and Desired speed decision................ 49 Figure 45 – Confidence interval for bicycles on a 95% level with modification of the desired speed and speed reduction zones. ......................................................................................... 51 Figure 46 – Data collection for Travel time and delay in Vissim. ........................................... 53 Figure 47 – Average Travel Time per bicycles comparison of Design A and B. ...................... 54 Figure 48 – Average Travel Time per buses comparison of Design A and B. .......................... 54 Figure 49 – Average delay per bicycles comparison of Design A and B. ................................ 55 Figure 50 – Average delay per buses comparison of Design A and B. .................................... 55 Figure 51 – Average Travel Time per bicycle with input changes for bicycles and buses. ...... 57 Figure 52 – Average Travel Time per bus with input changes for bicycles and buses. ........... 57 Figure 53 – Average delay per bicycle with input changes for bicycles and buses................. 58 Figure 54 – Average delay per bus with input changes for bicycles and buses. ..................... 58. ix.

(13) List of Tables Table 1 – Parameter description for the Wiedemann 99 following model in Vissim with the corresponding values from COWI. ......................................................................................... 17 Table 2 – Mean speed estimation for bicycles and motorized vehicles. ................................. 27 Table 3 – Number of vehicles during 07:30-08:30.................................................................. 27 Table 4 – Shows the traffic signals are the cycle length, green time, red time and the redamber time. ........................................................................................................................... 28 Table 5 – Shows the average flow for different vehicles. ....................................................... 35 Table 6 – Number of replications calculation ........................................................................ 40 Table 7 – The average speed of the field measurement and the simulation output for motorized vehicles. ................................................................................................................ 41 Table 8 – The average speed of the field measurement and the simulation output for bicycles with default settings from COWI. .......................................................................................... 42 Table 9 – Adjusted driving behaviour parameters compared to COWI settings. ................... 44 Table 10 – The average speed of the field measurement and the simulation output for bicycles with driving behaviour parameter adjustments. ...................................................... 45 Table 11 – The average speed of the field measurement and the simulation output for bicycles with changes in acceleration and deceleration ........................................................ 47 Table 12 – The average speed of the field measurement and the simulation output for bicycles with changes in desired speed and speed reduction zones. ..................................... 50 Table 13 – Shows the different input for Design A and B of Bicycles and Buses. ................... 56. x.

(14) 1. Introduction Bicycles are considered to be the most efficient transportation mode for limiteddistance trips due to its relatively low price and maintenance costs and no running cost, as well as its flexibility in terms of paperwork to obtain and use a bicycle, and easy to drive and its variable parking options. Bicycles are also known for their zero-emission on road compared to other vehicular modes, which makes it one of the most environmental-friendly transportation modes (UN Environment, 2016). Over the past two decades, the use of bicycles as a mode of transportation has increased worldwide and become more and more popular (Aldred, Best, & Jones, 2016). Therefore, planning and building adequate bicycles-related infrastructure has become a growing need in most cities, aiming to meet the growing demand, and to encourage the use of bicycles for longer distances. As in most capitals and major cities , the population of Stockholm is increasing, and so is the demand for transportation (Stockholms stad , 2019 B). The steadily increasing road upgrade projects and transportation infrastructure such as subways structures and buses facilities are insufficient to meet the increasing transportation demand (Stockholms stad, 2020). With Stockholm’s limited space for new roads and inability to provide additional lanes due to width of the road, one of the option authorities across Europe are opting for is to provide dedicated bicycle lanes. However, even with dedicate lanes, possible conflicts between bicycles, motorized vehicles, and pedestrians might still exist, especially at intersections and bus stops. A common conflict between buses and bicycles occur when buses need to cross through the dedicated bicycle lane to reach the bus stop as illustrated in Figure 1, where the red circles represent the points where the conflicts occur. In this xi.

(15) design, the bicycle lane is located on the left side of the bus stop in the upstream direction.. Figure 1 – Bicycle lane on the left side of the bus stop in the upstream direction (Trafikverket, 2020 A).. The buses stop frequently at specific locations with different acceleration and deceleration. The frequent pulling in and pulling out of the buses from stops create various impacts on the road users. The most noticeable impact is the conflict between the buses and bicycles when bicycles use the outer lane and buses need to cross the bicycle lane, which might delay the movements of bicycles and create queues of bicycles behind the buses. The conflict points, which are the red circles between the buses and bicycles in Figure 1, are also hazardous spots that may increase the risk of accidents, especially when the number of buses and bicycles are too high. One way of dealing with this conflict is to locate the bicycle lane to the right side of the bus stop in the upstream direction as shown in Figure 2. Moving the bicycle to the right side in the upstream direction would create a new conflict between bicycles and pedestrians who need to cross the bicycle lanes to reach the bus stop. However, each layout has its advantages and disadvantages. One way to measure the performance and evaluate a bus stop layout design is by the travel time and delay of different modes of transportation and pedestrian.. 2.

(16) Figure 2 – Bicycle lane on the right side of the bus stop in the upstream direction (Trafikverket, 2020 A).. 1.1 Aim and research questions The aim of this thesis is to study and analyze two road layout designs of bus stop. Both designs deal with the conflict between the position of the bus stop and the dedicated bicycle lane. These layouts are presented in Figure 1 and Figure 2 and will be referred to as Design A and B. The analysis will include comparisons of the impacts caused by each design quantified in terms of travel time and delay and delay for both bicycles and buses. The following research questions will be investigated in order to achieve the aim of this thesis: •. What impact do the two layout designs have on the bus stop, bicycles and buses in terms of travel time and delay?. •. How can a conflict between bicycles, buses and pedestrians be modelled in a micro-simulation software program?. •. Which layout design should be preferred if the number of bicycles and buses are increased?. 1.2 Methodology A literature review on bus stop layout design is first studied. The investigated designs are as recommended by (Trafikverket, 2020 A). To assess the impact of each design, a microscopic traffic simulation study is carried out. Both designs are modelled in a microscopic traffic simulation software, in this case Vissim. To carry out the microscopic traffic simulation study, a location with a similar layout as those recommended in the literature review is selected as a study case. 3.

(17) Data from the traffic on the selected location is collected using video cameras and the same is used to calibrate the model in Vissim. The result from this calibrated model will then be compared to an alternative bus stop design. The theory required to carry out a microscopic traffic simulation is studied and presented as part of the theoretical background, which includes the calibration method and statistical analysis for hypothesis tests. The modelling of conflicts between bicycles and vehicles in Vissim is investigated, and since one of the bus stops designs deals with conflicts with pedestrians, a method to model this particular conflict is proposed. Lastly, a prognosis for higher traffic volumes for both bicycles and vehicles is performed to verify if one design is preferable over the other under different traffic conditions. This will be referred to as experiments in this thesis.. 1.3 Study area The chosen area to study is Langholmsgatan Street, which is located in Sodermalm area in Stockholm. The street includes a bicycle lane stretching from south of Sodermalm to Kungsholmen and has the second largest volume of daily cyclists in Stockholm, with approximately 15 220 passengers per day in 2018 (Stockholms stad, 2019 A). Studying a location that has a high number of bicycles per day will have impact on travel time and delay. The layout of the bicycle lane in this location is similar to Figure 1 where the conflict arises when buses need to cross through the bicycle lane to reach the bus stop at Hornstull. Figure 3 and Figure 4 show where the bus stop and the bicycle lane are located in the study area. The red line in Figure 3 represents the bicycle lane, while the green area marks the bus stop. There are also three traffic lights shown as red circles in Figure 3.. 4.

(18) Figure 3 – Plan view of the study case (Långholmsgatan).. Figure 4 – Sideview of the study case (Långholmsgatan).. 5.

(19) 1.4 Research Limitations and assumptions For modeling and analysis simplification, cars, trucks and motorbikes will be modelled and simulated but they will not be counted separately. Cars, trucks and motorbikes will be counted together, and they will be referred to as motorized vehicles. This is due to a low influence from motorized vehicles on the performance of bicycles. Another assumption is that three weekdays of data collection will be enough to show the morning peak hour and it is assumed to be representative for the peak hour. Also, the research limitations include the assumption that buses will only exit the bus stop at the end of the platform while in reality, buses may leave the bus stop at any available section of the platform. Another limitation is that the number of pedestrians and their speed are not included in the model due to complications of measuring and extracting these data from a video recording. An assumption is that the number of pedestrians trying to board the bus will be set to 500 passengers per hour. This figure has been considered to represent the pedestrians for the best possible results. Bus sizes considered in the model are standard size and articulated bus size with fixed capacity set to 110 and 180 passengers respectively which is default setting in Vissim. The two bus sizes are considered to reflect the actual two main types of buses observed on site. The number of boarding and alighting passengers was assumed to be 30 and 40 for standard and articulated size respectively, as measuring such activity was impossible to be extracted from the recorded videos. Another limitation is that the bicycles will only go in one direction which is referred to as the upstream direction. Finally, the study was limited to the modelling analysis and assessment, and no full traffic safety assessment is considered.. 1.5 Structure of the report The work in this thesis is presented as follows: The thesis starts with a literature review part which contains of chapter 2 and 3. Chapter 2 is about the bus stop layout guidelines including the recommended design from The Swedish Transport Administration for bus stops. Chapter 3 covers traffic simulation with focus on microscopic traffic simulation in Vissim including driving behavior, lane changing and parameters of bicycles in Vissim. 6.

(20) This chapter includes pedestrian behavior and conflict modelling in Vissim. Chapter 4 describes how the data was collected. Chapter 5 illustrates how the model is developed in Vissim along with its calibration. Chapter 6 includes result and analysis about Design B and the experiments of Design A and B. Chapter 7 contains the discussion and conclusion.. 7.

(21) 2. Bus stop layout guidelines In this chapter, relevant literature for this thesis is presented. The chapter provides information about bus stop layout guidelines. Krykewycz (2010) states that conflicts between buses and bicycles particularly occur at points where buses cross bicycle lanes to pull into or pull out from bus stops. Bicycles can be forced to sway unsafely into motorised lanes. Keeling, Glick & Miguel (2019) also states that, it is complicated when buses and bicycles share the same space, as this not only magnifies the bicycles safety risks, but also it creates significant delays for buses beside the hassle on bus drivers to interact with the surrounding bicyclists. On city streets, bicycles and buses are in several ways natural enemies and they often operate in the same space (the right side of the street) and at roughly the same speeds over significant stretches of road and this could lead to a variety of conflicts between bicycles and buses ( Delaware Valley Regional Planning Commission(DVRPC), 2009). Either bicycles or the buses could have right of way on another and law might be different from each country. In USA, the law says that neither vehicle has universal priority and the vehicle being overtaken has (Krykewycz, 2010). This means that if a bus is attempting to cross a bicycle lane, it needs to yield for a potential bicycle or vice versa. Buses should not accelerate around a bicyclist and then cut them off while curbing. On the other hand, bicyclists should not overtake a bus as it approaches an intersection and expect the bus to yield. According to Swedish traffic regulations, buses driving in cities have priority when pulling out from a bus stop and other traffic must give way for the bus to reach the far-right lane of the road (NTF, 2020). There have been a variety of strategies implemented worldwide to address the specific bike and bus conflict but DVRCP (2009) mentions some various strategies in order to solve the conflict. 8.

(22) One strategy is referred to as ‘’Coloured bike lanes in conflict hotspots, including transit stop areas’’. This strategy has been used by a number of cities with coloured lane treatments in bicycle lanes and especially where vehicles cross and conflicts are common. Another strategy was called ‘’Physical re-routing of bike lane around stop location’’. This strategy involves the bicycle lane to be routed onto sidewalk, outside the bus stop. However, this strategy would have limited applicability to cities with typical narrow and small streets. But the strategy might be appropriate in cities where the rights-of-way are wider and heavy bicycle traffic might justify the expense. The Swedish transport administration has a unit called VGU (vagar och gators uformning) which develops rules and recommendations for a street or a road (Trafikverket, 2020 A). The rules are compulsory when working with the state roads. For the municipalities, VGU is voluntary and provide advisory publications (Trafikverket, 2019 B). The choice of bus stop type in an urban environment depends according to Trafivkerket (2020 A) on the amount of traffic, but also on the reference speed, pedestrian and bicycle traffic, local priorities, the nature of the city, etc. One way to design the bus stop with passing bicycle traffic is to place the bicycle lane to the left of the bus stop in the upstream direction and merged with traffic. If this design is applied, the buses must cross the bicycle lane in order to reach and exit the bus stop, see Figure 5. The bicycles must yield for crossing buses so that the buses can change lanes. This design is mainly used on the main network in urban areas when car traffic is prioritized, and bicycle traffic is extensive. Advantages with this solution is that buses do not block cars, trucks etc. when it is at the bus stop and there is relatively good safety and comfort for the passengers waiting for the bus . One disadvantage is uncomfortable driving to get to the bus stop for the buses.. Figure 5 – Bike lane between the bus stop and traffic lane (Trafikverket, 2020 A).. 9.

(23) VGU (2020 A) states that car traffic should be separated from pedestrians and bicycles along bus stops to improve the level of safety and to avoid the risk of accidents, given the relatively high traffic speed. Higher flow and speed increase the importance of the separation between them. One way to separate the bicycles and buses is to place the bicycle lane to the right of the bus stop in the upstream direction if enough safety devices (preferably in the form of fences) are available to reduce the conflict with pedestrians. The pedestrians need to cross the bicycle lane in order to reach the bus stop. In this way, a new conflict is created between pedestrians and bicycles where bicycles must yield for crossing pedestrians at the crosswalks. This is illustrated in Figure 6. Cross walk is provided on either side of the stop where the railing starts and ends (Trafikverket, 2020 A). Placing the bicycle lane to the right of the bus stop would eliminate all bus-bicycle conflicts. Unfortunately, this configuration is considered to be the best for wide roads, as it requires a huge amount of right-of-way (Miguel , Keeling, & Glick, 2019).. Figure 6 – Recommended bus stop for a higher number of passengers (Trafikverket, 2020 A).. 10.

(24) 3. Microscopic traffic simulation In this chapter, relevant literature for this thesis is presented. This chapter consists of two sections, the first section gives a brief summary of the activities in a microscopic traffic simulation and how it can be carried out. The second section will include information about how microscopic traffic simulation can be modelled in Vissim. A traffic micro-simulation model consists of sub-models and various parameters which describe the driving behavior of humans. Examples of sub-models are lane-changing and car-following. Lane-changing models describe the behavior of drivers when it is critical to decide whether to change lane or not. The car-following model describes the interactions with preceding vehicles in the same lane (Janson Olstam & Tapani, 2004).. 11.

(25) 3.1 Activities in a microscopic traffic simulation When building a microscopic traffic simulation model, a few steps can be formulated in order to finish and make conclusions. Figure 7 shows an example from Olstam (2019) of the steps in a traffic simulation.. Figure 7 – Building a simulation model steps (Olstam, 2019). The development of the base model will be further discussed in chapter 5. The adjusted parameters will be also presented in model calibration section 5.4. Verification is a process to ensure that no mistakes have been made, e.g. programming errors, encoding errors, input data errors (Olstam, 2019). Compare model to data is a comparing of data set where three situations are needed according to Olstam (2019); calibration, alternative analysis and 12.

(26) validation. Calibration is the comparison of a set of simulation output data with a set of measurements to improve the accuracy of the model. Alternative analysis is the comparison of two (or more) sets of simulation output data from different scenarios to find the “best”. Validation is the comparison of a set of simulation output data with a set of measurements to test the calibration. In order to compare the data, statistical methods can be applied for comparison of data sets like confidence intervals and statistical hypothesis test which will be presented in section 5.4. Another part of the calibration is to determine the number of replications which are needed to run the model. The process of determining number of replications will be further discussed in section 5.4.. 3.2 Microscopic traffic simulation in Vissim In this section, necessary information regarding how a traffic simulation of a bus stop in Vissim is presented. The first and second subsection are sub-models which are used to describe driving behavior. The first sub-model is referred to as the car-following model. A car-following model controls driver’s behaviour with respect to the preceding vehicle in the same lane. The second sub-model includes information about the lane changing which describes drivers’ behaviour when deciding whether to change lane or not (Janson Olstam & Tapani, 2004). The third subsection illustrates previous studies of different bicycle parameters in Vissim. The fourth section consists of how pedestrian can be modelled in Vissim. The fifth subsection includes information about how a conflict between bicycles, buses and pedestrians can be modelled.. 3.2.1 Car-following model in Vissim In Vissim, two car-following models can be used which are Wiedemann 74 and Wiedemann 99. The main difference between the two car following models according to Fransson (2018), are that Vissim tries to create a more diverse driver population. The Wiedemann 99 model is also more up to date and popular (Gao, Liu, & Feng, 2012). Wiedemann 99 shows the processes of human perception and decision making. The model avoids sudden changes in speed and contains a threshold for perception to identify realistic behavior of road users (Hamm, 2016). Wiedemann 99 describes the psycho-physiological aspects of the driving behavior in terms of four discrete driving regimes: free flow, approaching slower 13.

(27) vehicles, car-following near the steady-state equilibrium, and critical situations requiring stronger braking actions (Treiber & Kesting, 2013). The regimes can be defined by different thresholds which can be seen in Figure 8 from PTV (2018).. Figure 8 – Wiedemann 99 model (PTV AG, 2018).. Ax is the desired distance between two standstill vehicles. ABX is the minimum distance between two vehicles which are traveling at equal speed. SDX is the maximum following distance. SDV is a point when a driver realizes he is close to a vehicle. CLDV is the point where a driver is aware of small differences in speed when the distance between the vehicle Infront decreases. OPDV is the point when a driver realizes that his speed is slower than the vehicle Infront (Franson, 2018). The thresholds can also be summarized according to Palmqvist (2015) as:. •. Following. •. When the driver catches up a slower-moving vehicle, he adapts the driver speed and brakes to finally reach the safety distance the driver is aiming for. Free driving The vehicle is not affected by the vehicle ahead but the driver trying to reach their target speed. In fact, this velocity cannot be kept completely constant but oscillates, which is built into the model.. •. Closing in When the driver catches up a slower-moving vehicle adapts the driver 14.

(28) •. speeds up this and slows in to eventually end up on the safety distance that the driver is trying to reach. Braking Braking occurs if the vehicle comes closer to the front vehicle than the desired safety distance. This happens when the vehicle in front is slowing down or if file changes are made in front of the driver, which in turn affects the speed of the driver.. The Wiedemann 99 model, according to Fransson, expressed mathematically as Equation (1). 𝐶𝐶8 − 𝐶𝐶9 𝑢𝑛 (𝑡) + 3.6(CC8 + 𝑢𝑛 (𝑡))∆𝑡 80 𝑢𝑛 (𝑡 + ∆𝑡) = 𝑚𝑖𝑛 (1) 𝑠𝑛 (𝑡) − 𝐶𝐶0 − 𝐿𝑛−1 3.6 { 𝑢𝑛 (𝑡) 𝑢𝑛 (𝑡 + ∆𝑡) is equal to the minimum of two speeds. The upper speed considers the restriction of the acceleration and the difference between CC8 and CC9. CC9 is the maximum acceleration when the vehicle is driving at 80 km/h and CC8 maximum acceleration when the vehicle is driving at 0 km/h. The other speed is calculated when the model is in the condition of steady state with CC0 and the distance(L) between two vehicles. CC8, CC0 and CC9 are model parameters among other parameters that are used in the Wiedemann 99 model.. 3.2.2 Lane changing The decision of making a lane change for a vehicle is quite complex. The first lane change model was developed by Gipps in 1986 where three questions can be formulated: •. Is a lane change possible?. •. Is a lane change necessary?. •. Is a lane change desirable?. The first question must undoubtedly always be answered with yes because in order to make a lane change since collision might otherwise occur. The other two questions are not necessarily easy to answer and might not be relevant in all cases. If a lane change is necessary or desirable depends mainly on factors like the current speed and lane. Some lane types might require specific decisions or maneuvers when making a lane change. Lane changing in Vissim consists of two different approaches: Necessary lane change and Free lane change. For the Necessary lane change, Vissim checks the 15.

(29) desired safety distance to the trailing vehicle on the new lane. The desired safety distance depends on the speed of the vehicle that wants to change the lane and on the speed of the vehicle preceding it. For a necessary lane change, the driving behavior parameters contain the maximum acceptable deceleration for a vehicle and its trailing vehicle on the new lane. The deceleration depends on the distance to the emergency stop position of the next route connector (entrance ramp). Regardless of which lane change model that are used, each model first needs to find a suitable gap in the direction of travel. The gap size depends either on the speed of the vehicle changing the lane or the speed of the vehicle approaching from behind on the lane (PTV AG, 2018).. 3.2.3 Parameters of bicycles in Vissim Microscopic traffic simulation has most been used to study the ability to model road user but most studies have not achieved this for bicycles (COWI, 2013). COWI (2013) did a bicycle study in Copenhagen and the report was according to Best, Aldred and Jones (2016):’’ A successful application to model cyclist flows’’. The goal from the study was to translate the results from the data collection into updated and validated parameters that can be used to simulate cyclists in VISSIM. The study has shown that the spread of the speed distribution is larger than the default settings. Acceleration seemed to be too high compared with cars and was reduced. COWI (2013) identifies ten key parameter groups/areas for the microsimulation of bicycles; vehicle characteristics, speed distributions, acceleration (and deceleration) distribution, following parameters, overtaking parameters, behaviour at narrowing sections, behavior at bus stops, behaviour in waiting zones, behaviour at stop lines and behaviour at right turns. The group ‘’Behavior at bus stops’’ shows different rules and parameters which should be used at bus stops. The parameters include desired speed and desired speed reductions for bicycles. It also shows that there was a difference in the behavior of bicycles at small and large bus stops. Most bicycles reduce their speed when they approach small bus stops and make weaving maneuvers when passengers enter or leave a bus, while the rest of bicycles make a full stop. Bicycles at large bus stop are more likely to make full stops The following includes different parameters in the car-following model as shown in Table 1 with the corresponding bicycle values from the COWI (2013) study.. 16.

(30) Table 1 – Parameter description for the Wiedemann 99 following model in Vissim with the corresponding values from COWI.. Parameter CC0 CC1 CC2. CC3. CC4 CC5 CC6 CC7 CC8 CC9. Description The standstill distances. This parameter is adjusting the distance from vehicle to vehicle at zero speed. Headway time. The parameter adjusts the time headway ( time between two following vehicles ). Restricts the distance difference a driver allows for before he intentionally moves closer to the car preceding him. Threshold for entering “following”. The value of this parameter explains in what distance the vehicle moves into the state of following another vehicle. When the distance exceeds this threshold. the vehicle is affected by the vehicle in front. Negative “following” threshold. Defines negative speed difference during the following process. Low values result in a more sensitive driver reaction to the acceleration or deceleration of the preceding vehicle. The same as CC4 but positive. This parameter decides how much a vehicles speed is varied depending on the distance to the vehicle in front. This parameter describes how much the acceleration is depending on the distance to the vehicle in front. Standstill acceleration. The acceleration a vehicle has when starting from standstill. Acceleration 80 km/h. The acceleration of a vehicle when it is traveling in 80 km/h.. COWI values 0.20 m 0.5 s 2.0 m. -20. -0.25 0.25 1 m/s2 0.2 m/s2 1.8 m/s2 0.01 m/s2. 3.2.4 Pedestrian behaviour modelling in Vissim Vehicles and pedestrians move on links in VISSIM. Since pedestrians can move sideways compared to vehicles, it is not appropriate to model them on links. Instead they can be modelled on area elements, which have the ability for movements in all directions (EIDMAR & HULTMAN, 2014). In 1995, Helbing and Molnar developed a model for the behavior of pedestrians called Social force model. Each pedestrian is appointed with a desired destination and a desired speed which is called driving force. The desired destination demands a route for each pedestrian. This route involves a possible obstacle or other pedestrians which will impact each pedestrian with a force called repulsive force. The forces can be visualized by Figure 9 from Diamakis and Guðmundsdottir (2018). 17.

(31) Figure 9 – Social force model (Dimakis & Guðmundsdóttir, 2018).. Diamakis and Guðmundsdottir (2018) also include the behavior of pedestrian to be based on three levels of detail:. •. Strategic The strategic level includes a pedestrian plan which operates on the level of minutes and hours where pedestrians are predefined with origin, destination and desired speed.. •. Tactical The tactical level where pedestrian decide their route on the seconds to minute level. This level also takes into consideration of the routing conditions which include obstacles and another pedestrian.. •. Operational The operational level decides the route by millisecond to seconds where it tries to avoid obstacles. The calculation is made constantly during the simulation and by every interaction with obstacles and pedestrian.. The social force model controls the operational level and parts of the tactical level in Vissim. The social force model consists, according to Diamakis and Guðmundsdottir (2018) of two parameters; local and global. The global parameters affect each pedestrian while the local parameters affect individually. 18.

(32) Especially tau and lambda are important parameters for the behavior of pedestrians. Tau represents the relaxation time or inertia that can be related to a response time, as it couples the difference between desired speed and desired direction. Lambda governs the amount of anisotropy of the forces from the fact that events and phenomena in the back of a pedestrian do not influence him (PTV AG, 2018). An illustration of two different lambda values can be seen in Figure 10 from Diamakis and Guðmundsdottir (2018). When lambda is equal to zero, something that happens in the back, has no impact at all on the pedestrian. When lambda is equal to one, every action from all surrounding pedestrian or obstacle has an effect.. Figure 10 – An illustration of two different lambda values in the Social force model (Dimakis & Guðmundsdóttir, 2018).. 3.2.4.1 Pedestrian routing decisions Pedestrians can walk in the same direction and on every area of the pedestrian link and area. This can lead to problem when all pedestrians want to walk on the same place of the pedestrian link. One way to force the pedestrians to walk in a specific order is to use partial pedestrian routing decisions (PTV AG, 2018). Partial pedestrian routing decisions start from a certain point and ends with a destination point. Partial pedestrian routing serves the local distribution of pedestrians without changing the pedestrian OD matrix. It is possible to increase the destination points in order to force the pedestrians to walk exactly as preferred (PTV AG, 2018). In Figure 11, an example from PTV (2018) of a partial pedestrian routing is illustrated. The route course is shown as a yellow line and the blue dot is an intermediate point which traverse the pedestrians. 19.

(33) Figure 11 – Example of a partial pedestrian routing (PTV AG, 2018).. 3.2.5 Conflict modelling in Vissim A conflict between two vehicles or a vehicle and a pedestrian can be modelled in Vissim either using the priority rules or conflict area. The priority rule is not controlled by signals and is used in situation when vehicles in different links or connectors need to consider each other (PTV AG, 2018). Figure 12 from PTV (2018), shows the theoretical framework of the priority rule. A headway distance and a gap time is used to control the vehicles. If a vehicle has entered the headway distance or if the gap time is not satisfied, vehicle on the other link will stop at the stop line. Gap time is the required time for a vehicle to reach time conflict marker with its current speed (PTV AG, 2018). The priority rule according to Virginia Department of Transportation (2020), is often used when geometric complexities can make conflict areas difficult or impossible to apply (Virginia Department of Transportation , 2020).. 20.

(34) Figure 12 – Theoretical framework for the priority rule (PTV AG, 2018). Conflict areas are easier to be applied to a network and are enough for dealing with a lot of vehicle types. Conflict areas can be located wherever two links are emerging. Vissim will automatically decide and determine the priority for each vehicle depending on the link that has right of way. The status of each conflict area will be showed by a color. Green indicates main flow (right of way) and red indicates minor flow(yield). If both are red, there is no right of way, as vehicles simply remain in their original sequence. If both are yellow, it is a passive conflict area without right of way. The conflict area is be based on some attributes such as speed, look ahead distance, time gap, safety distance etc. Each calculation consists of a calculation where a vehicle from a minor traffic stream tries to enter a mainstream. In this case, safety distance is important and if this distance is too small, the will vehicle will decelerate if it must stop in front of the conflict area. Each time a vehicle tries to enter the main flow, braking is either cancelled or the driver continues driving and might even accelerate, e.g. when finding a gap in the traffic stream to enter. In Figure 13 from PTV (2018) , the conflict area is applied to a roundabout with difference status of each conflict area.. 21.

(35) Figure 13 – Example of a roundabout with the use of the priority rule (PTV AG, 2018).. 22.

(36) 4. Data collection The field observations were obtained from video cameras and were used as input for the analysis using VISSIM. Data was collected by extracting the relevant data from the cameras that were installed on top of a pedestrian bridge across Langholmsgatan (the road on which the studied bus stop is located), presented by the red circle in Figure 14. The data extraction was carried out for three different days during the morning peak hour between 07:00 AM and 08:30 AM at 4th, 24th and 25th of March 2020. Pavement markings, road signs,, geometry and other features were obtained from Google Earth. This chapter gives summary of the collected data which includes traffic flow (volume), speed and traffic signals timing.. Figure 14 – Shows the bus location & the location of video recording position.. 23.

(37) 4.1 Speed and flow One of the parameters that is to be estimated was the speed. The speed can be estimated according to Treiber and Kesting (2013) by the help of two quantities:. • •. The time 𝑡𝛼0 from where a vehicle is passing a point. The time 𝑡𝛼1 from where a vehicle is passing the next point.. By assuming an average vehicle length L, it is possible to estimate a speed for an individual vehicle by using the time difference between the first single point and the next point. The vehicle length L can also be interpreted as the distance between two points. Equation (2) defines the relationship between these parameters. 𝐿. 𝛼 𝐿𝛼 = 𝑣𝛼 (𝑡𝛼1 − 𝑡𝛼0 ) −→ 𝑣𝛼 = (𝑡 1 −𝑡 0) 𝛼. (2). 𝛼. The relationship can also be visualized when a single vehicle is passing two points, see. Figure 15 from Treiber and Kesting (2013).. x. 𝑡𝛼0. 𝐿𝛼 𝑣𝛼. 𝑡𝛼1. t. Figure 15 – Single vehicle data measurements (Treiber & Kesting, 2013).. The field speed observation was estimated using Equation (2) by considering two identified points.. 24.

(38) The green circles in Figure 16 show the location of the two points which are used to estimate the speed for a single vehicle. The time needed to pass through the two points gives the travel time used in Equation (2), with a total distance of 75 meters.. Figure 16 – Shows the passing points of the speed estimation and distance between them visualized as an arrow.. This process was made for 100 vehicles each day for three days and for vehicle type (bicycles and the motorized vehicles) as a sample test under the assumptions that most of the motorized traffic share identical speed. In total, the speed of 300 bicycles and motorized traffic were estimated and can be visualized in a cumulative distribution curve (CDF). A CDF curve shows a distribution of a certain quantity and a probability that the quantity will take a value less than or equal to zero. Also notice that the speed for buses could not be measured due to difficulties of measuring it in this area separately. Figure 17 shows the CDF curve for the observed speed of the 300 estimated motorized traffic and Figure 18 shows the CDF curve for the observed speed of the 300 estimated bicycles. 25.

(39) CDF for the speed of 300 motorized traffic 1 0.9 0.8. Percentage (%). 0.7 0.6 0.5. 0.4 0.3 0.2 0.1 0 13.00. 18.00. 23.00. 28.00. 33.00. 38.00. 43.00. 48.00. 53.00. Speed (km/hr) Figure 17 – The cumulative distribution curve CDF for motorized traffic.. CDF for the speed of 300 bicycles 1 0.9. 0.8. Percentage (%). 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 10.00 12.50 15.00 17.50 20.00 22.50 25.00 27.50 30.00 32.50 35.00 37.50 Speed (km/hr) Figure 18 – The cumulative distribution curve CDF for bicycles.. The estimation of the mean speed for bicycles and motorized vehicles can be visualized in Table 2. The number of bicycles (volume), motorized vehicles and 26.

(40) buses between 07:30 and 08:30 for each day can be seen in Table 3. It should be noted that in Table 3, buses are not included in motorized vehicles and are measured separately. Table 2 – Mean speed estimation for bicycles and motorized vehicles.. Mean speed estimation Day. Mean speed bicycles (km/h). Mean speed for motorized vehicles (km/h). 4 of mars 2020. 19.93. 35.16. 24 of mars 2020. 20.83. 36.20. 25 of mars 2020. 21.58. 35.60. Table 3 – Number of vehicles during 07:30-08:30. Time. Bicycles. Motorized vehicles. Buses. 4 of mars 2020. 479. 1044. 37. 24 of mars 2020. 497. 822. 16. 25 of mars 2020. 525. 948. 18. Average. 500. 938. 25. 4.2 Traffic signals The study area has three traffic signals, the first one is for the intersection in the beginning of the study area while the other two are midblock signalized pedestrian crossing signals. Figure 19 shows the different traffic signals, which are referred to as signal 1, 2 and 3. Considering the traffic signal is important because it dictates when bicycles and buses might interact. Due to the traffic signal, bicycles will always arrive in fleets to the bus stop which may delay a big proportion of the bicycles when they interact with a bus. The measured time from the traffic signals are the cycle length, green time, red time and the red-amber time. The measured time for cycle length and green time can be seen in Table 4. Red-amber time corresponds to the remaining time of the flashing signal.. 27.

(41) Table 4 – Shows the traffic signals are the cycle length, green time, red time and the red-amber time.. Signal 1. Signal 2. Signal 3. Cycle Length (sec). 100. 100. 100. Green Time (sec). 26. 68. 68. Red Time (sec). 68. 26. 26. Amber (sec). 3. 3. 3. Red-Amber (sec). 1. 1. 1. Figure 19 – Shows the location of three traffic signals.. 28.

(42) 5. Simulation model development This chapter describes how the model was developed using Vissim along with related calibration for the model. The chapter starts with the development of Design A and B and ends with a calibration.. 5.1 Design A The layout of Design A can be seen in Figure 20 while Figure 21 shows a 3D view of the model. The bicycle path is provided to the left of the bus stop in the upstream direction of traffic. The lanes for cars, trucks and buses are located to the left of the bicycle path in the upstream direction. The geometry of the baseline model is based on estimation from google maps and google earth. In some cases, the lane widths, length of lanes and crosswalk widths were measured directly from site using a meterstick. The green area represents walking area for the pedestrians which also consists of an entry and exit area. The blue area represents waiting area where pedestrians wait for the arriving bus. The pink area is representing an area where pedestrians can enter a bus. The yellow area represents speed reduction zone in where vehicles will reduce or their speed while maneuvering a curve or a making a turn. Red and green area in the model represents conflict areas and are used to dictate the right of way when two links merges. Objects on the red colored link yields to the object on the green colored link. In the case of ‘’Design A’’, conflict areas are only applied to the conflict between buses and cars where buses will have the right of way when entering the traffic lane. 29.

(43) Figure 20 – Vissim for Design A.. Figure 21 – 3D view for Design A.. 30.

(44) Design A is modelled with default settings from Vissim (2018) regarding driving behavior parameters, acceleration and deceleration for motorized vehicles. Driving behavior parameters, acceleration and deceleration for bicycles are set according to COWI (2013).The pedestrian behavior parameters will also be set to default according to Vissim (2018). As mentioned in section 3.2.5, modelling conflicts in Vissim can either be done with priority rules or with conflict areas. The conflict between bicycles and buses is modelled using the priority rule. Site observations show that bicycles always yield for buses when a bus will try to cross the bicycle lane. Site observations show that buses only give way to bikes if the bicycles are right next to the bus, however, if the bicycles are behind or in front, the bus will not give any priority to the bikes. This two phenomena or observations are easier to model with the priority rule since it is possible to use markers and lines in order to dictate where buses and bicycles must stop. Figure 22 shows the priority rules when buses need to enter the transit stop. Notice different stop lines, headway and conflict markers which will govern that bicycles or buses yield for each other. A problem occurred when buses had left the conflict marker and a bicycle was close to the stop line. Bicycles would continue to drive even though it can lead to a crash with the oncoming buses. This was solved by implementing an additional priority rule for bicycles where buses will give way for bicycles. This priority rule can be seen as the blue text boxes in Figure 22. The distance headway is 5.12 meters for the priority rule for buses (black boxes) and 2.5 meters for the priority rule for the bicycles (blue text boxes). The time gap is also implemented, which is set to 1 second for each priority rule.. 31.

(45) Figure 22 – Shows the priority rules location and the conflict between the bikes and the entering buses from the bus stop.. 5.1.1 Bus Time Scheduling The bus time scheduling in the model was based on the extracted data from the recorded videos. Two types of buses were observed and considered in the Vissim model. A standard bus size (red color) with a 110-seat capacity and an articulated bus size (blue color) with 180-seat capacity. The purpose of using two bus sizes is that the articulated bus is longer which will affect the time when bicycles must yield and wait for the bus to cross the bicycle lane. A longer bus needs more time than a shorter bus to cross the bicycle lane. The layout of the two buses is represented in Figure 24 and Figure 23 respectively. The buses are scheduled to arrive 13 times per hour for the standard bus and 12 times per hour for the articulated bus in a frequency of 5 minutes per bus type.. 32.

(46) Figure 24 – Shows standard bus size.. Figure 23 – Shows articulated bus size.. 5.1.2 Traffic Signals Modelling The traffic signals in VISSIM are modelled according to the information in section 4.2. Signal 1 has a different set of parameters for the cycle length, green time, red time and the red-amber time. Signal 2 and 3 have identical measured time property regarding green time, red time, amber time and red-amber time. All the three signals start at the same time but signal 1 has longer green time based on the observation data as shown in Figure 25 and Figure 26 respectively. All the signals were assumed to operate as fixed time signals.. Figure 25 – Shows the signal group for signal 1.. Figure 26 – Shows the signal group for signal 2 and 3.. 33.

(47) 5.1.3 Speed Modelling The speed that was assigned in the model is modelled as a cumulative distribution function (CDF). The CDF for bicycles and motorized vehicles will be modelled in such a way to mimic the CDF of bicycles and motorized vehicles found in figure 17 and 18 in data collection. The CDF for motorized vehicles and bicycles can be seen in Figure 27 and Figure 28. The y-axis represents the probability while the xaxis represents the speed. Each vehicle will be assigned to a speed based on a probability when they enter the network.. Figure 27 – Desired speed distribution for motorized vehicles.. Figure 28 – Desired speed distribution for Bicycles.. 34.

(48) 5.1.4 Vehicle Input For Design A, the hourly traffic volume considered for the vehicle input is based on the average of the collected data. The flow for bicycles, buses and motorized vehicles (cars and trucks) can be seen in Table 5. Buses, bicycles and motorized vehicles in the network will be generated 70 meters before the first traffic light(intersection Langholmsgatan and Horsnagatan). Table 5 – Shows the average flow for different vehicles.. Flow(vehicles/hour). Bicycles. Buses. Motorized vehicles. 500. 26. 938. 5.2 Design B In Design B, the bicycle lane is shifted to the right side of the bus stop and to the left of the walking area (green area) in the upstream direction as shown in Figure 29. Pedestrians who are entering or leaving the bus stop must pass the bicycle lane. In order to pass the bicycle lane, pedestrians must use a crosswalk which is located in the beginning and at the end of the bus stop in the upstream direction. This new design is based on the recommendations from VGU (2020 A) when there is a high pedestrian flow, see Figure 6 in chapter 2.. Figure 29 – Shows a 3D view for the Vissim scenario-2 model.. Design B does not have any conflict between bicycles and buses, though it has an additional conflict between pedestrians and bicycles. The conflict occurs at the 35.

(49) two crosswalks located in the beginning and at the end of the bus stop. The conflict point is illustrated by the red circles in Figure 30, where pedestrians will encounter oncoming bicyclists. Pedestrians will, according to trafikverket (2019 B) ,always have the right of way when passing a crosswalk. This means that bicycles in this design, always must yield for pedestrians.. Figure 30 – The conflict between pedestrians and bicyclists in Design B.. In Design B, the bicycle lane needs to be relocated. The current pedestrian area (Green and blue area) and the walking area needs to be reduced, however, it is assumed that the walking area (green area) is still sufficient to maintain the pedestrian volumes. The new conflict in this design will also be modelled with a priority rule at the crosswalks. Speed reduction zones will also be included close to the crosswalks since the bicycle lane is curved before and after the crosswalks, this is necessary to slow down the cyclists in order to enable them to safely maneuver the curve. Signal heads for bicycles will be ignored in this design since the bicycle lane is thought to be a part of the walking area. Figure 31 shows the first crosswalk in the upstream direction (beginning of the transit stop) and Figure 32 is the second crosswalk in the upstream direction (end of the transit stops). Notice the pedestrian routing decisions, which will force the arriving pedestrians to walk on the right side of the crosswalk and the departing pedestrian will walk on the left side of the crosswalk, see Figure 31 and Figure 32. The pedestrians will also be prioritized when bicycles are approaching. The bicycles will give priority and wait for pedestrians if a bicycle is 2.5 meters from a 36.

(50) pedestrian, which will try to cross the crosswalk. The priority rule for the second crosswalk shares the same rules and details as the priority rule for the first crosswalk. Notice that Design B will use the same driving behavior parameter settings, desired speed, bus time scheduling table, flow and desired acceleration and deceleration as Design A.. Figure 31 – Shows the first crosswalk of design B.. 37.

(51) Figure 32 – Shows the second crosswalk of Design B.. 5.4 Model Calibration The calibration process consists of several different steps. First the number of replications is presented. Then, the main goal is to achieve a valid t-test and confidence interval for the measured speed of motorized vehicles and bicycles. This is done by changing some parameter values in the car-following model for bicycles, changing the desired acceleration and deceleration for bicycles and also modification of the speed reduction zones. For the t-test, the absolute value of the t-value |T| and the critical t-value will be compared with the hypothesis test investigation if there is a statistical significant difference between the mean value from the simulation and the mean value from measurement observations. The result of the test is either that the null hypothesis should be rejected(the absolute value |T| is larger than the critical value t) or that it cannot be rejected(the absolute value |T| is smaller than the critical value t). For the calibration this imply that if the null hypothesis should be rejected the model is not calibrated and further calibration efforts in terms of adjusting parameters are required. If the null hypothesis cannot be rejected the model can be said to be able to represent the observations, i.e. that the model is calibrated with respect to the metrics used in 38.

(52) the calibration process. There will also be a confidence level comparison between the simulation output means and field measurements means on a 95 % level. The speed is estimated in Vissim at a particular point in the network, see Figure 33. The brown line is the speed estimation for motorized vehicles and the yellow line is the speed estimation for bicycles. This point is chosen since it is in the middle of the two passing points, which are used for measuring speed in the field data collection.. Figure 33 – Speed estimation in Vissim. 5.4.1 Number of replications The number of replications can be calculated according Olstam (2019) as Equation (3). 𝑠𝑚 ∗ 𝑡𝑚−1 (𝛼/2) 2 𝑛= ( ) 𝑋𝑚 ∗∈. (3). Where n is the required number of replications, Sm is the standard deviation for the investigated performance indicator, Xm is the mean for the investigated performance indicator, ∈ the accepted error rate in terms of percent of the mean value and 𝑡𝑚−1 (𝛼/2) is the value from a student t-distribution for the confidence level (𝛼/2). The standard deviation s and the mean speed X are unknown but can be estimated by running a limited set of m simulations. 39.

(53) Standard error rate was set to 3 and t-value 𝑡0.05/2 was 4.3. Standard error rate was set to 3 since it should be between 2 and 5% for traffic according to Bernhardsson (2017) . Three simulation runs were made with the corresponding mean time 20 km/h and standard deviation 0,44, which resulted in 9.94 replications and was rounded up to 10. The calculation can be seen in Table 6. Table 6 – Number of replications calculation. Mean speed (km/h). t-value. Standard deviation. Accepted error rate. Number of replications. 20.0. 4.3. 0.44. 3%. 9.94 ≈ 10. 5.4.2 Motorized vehicles The calibration of the speed for motorized vehicles is made by a t-test and confidence interval on a 95 % level. Motorized vehicles use default settings in Vissim regarding car-following model parameters and desired acceleration and deceleration. Table 7 shows the average speed for the measured sample data (Field measurement) for three days and the average speed from simulation for 10 replications. It also shows the statistical hypothesis test (t-value) on a 95% level which can be seen at the bottom of the table. The t-value from the T-Distribution table in this case will be 2.201 and absolute |T| is 1.7. For the calibration, this imply that the null hypothesis cannot be rejected and there is significant difference and the model can be said to be able to represent the observations, i.e. that the model is calibrated with respect to the metrics used in the calibration process. A confidence interval test on a 95% level is also made between the field measurement and the simulation output which indicates overlapping and this can be seen in Figure 34.. 40.

(54) Table 7 – The average speed of the field measurement and the simulation output for motorized vehicles. Replication. Field measurement. Simulation km/h. km/h. 1. 35,16. 35,78. 2. 36,2. 35,75. 3. 35,6. 35,58. 4. 35,88. 5. 35,89. 6. 35,27. 7. 35,62. 8. 36,04. 9. 36,07. 10. 36,00. Average. 35,65. 35,79. |T|. 1,7. t. 2,201. Figure 34 – Confidence intervals for motorized vehicles on a 95% level.. 41.

(55) 5.4.3 Bicycles This section will describe the calibration process for bicycles. Initially, bicycles will be modelled with driving behavior parameters, desired acceleration and deceleration from COWI (2013).The corresponding confidence interval and t-test on a 95% level can be seen in Table 8 and Figure 35 below. Since absolute T is larger than the t-value, the null hypothesis that there is no difference can be rejected, hence there is a statistical difference between the field measurements and the simulation. Table 8 – The average speed of the field measurement and the simulation output for bicycles with default settings from COWI.. Replication 1 2 3 4 5 6 7 8 9 10 Average. Field measurement km/h 19,93 20,83 21,58. Simulation km/h. 20,78. 18,64. |T|. 17,1219. t. 2,2010. 18,74 18,84 18,39 18,40 18,91 18,63 18,61 18,79 18,42 18,66. 42.

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