Waiting time and headway modelling for urban transit systems – a critical review and proposed approach

Esfeh, Mohammad Ansari; Wirasinghe, S. C.; Saidi, Saeid; Kattan, Lina

doi:10.1080/01441647.2020.1806942

Cited by 38 publications

(14 citation statements)

References 74 publications

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“…This is usually divided into actual waiting time and perceived waiting time. For these two kinds of waiting time, the actual waiting time can be calculated by the headways, and the assumption that waiting time is calculated to be half the headways has been widely used ( 6 ). On the other hand, perceived waiting time is public transport users’ perceptions of how long/short this is; if they are offered some distractions, users may feel that the waiting time is shorter.…”

Section: Literature Reviewmentioning

confidence: 99%

Would You Wait? Bus Choice Behavior Analysis Considering Various Incentives

Pan

Waygood

Patterson

2022

Transportation Research Record: Journal of the Transportation R

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During the peak hour, ridership is much higher, and this can lead to problems such as overcrowding for customers and vehicle bunching for the operator as dwell times increase for the lead bus. In this paper, we examined whether to avoid overcrowding people might be willing to wait for the next bus knowing there were seats and if they were offered an incentive. Three distinct types of incentive were offered, defined according to goal-framing theory. To obtain the choice data, a discrete choice experiment was developed and incorporated into an online survey that was distributed to public transport users. A binary logit model and a mixed multinomial logit model were used to investigate how different factors influence public transport users’ bus choice behavior. Results show several considerations that increase the likelihood of users agreeing to the request to wait. These include the weather being sunny/fine, not very cold, or both, the purpose being seeing a friend or shopping, longer in-vehicle time, shorter proposed bus waiting time, and the incentive. The results of the mixed multinomial logit model show variation among the respondents with regard to the incentives. We found that age, gender, work status, possession of a driver ID, and vehicle availability are significant predictors for incentive preference. In that our models target specific groups of users with tailored incentives, the results indicate how to persuade public transport users to avoid overcrowded buses.

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Section: Literature Reviewmentioning

confidence: 99%

Would You Wait? Bus Choice Behavior Analysis Considering Various Incentives

Pan

Waygood

Patterson

2022

Transportation Research Record: Journal of the Transportation R

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“…where cdlri means the onboard passenger riding comfort degree in track section i of route r on train l, t lr i,i+1 means the running time of train l on route r between station i and station i + 1, and N means the number of stations in the railway network. And in urban public transit, based on Assumption 4 (the operation patterns of the trains adopt the same headway time in the same network), similar to the majority of the previous studies, we approximate the mean waiting time of passengers at stops as half of the headway between the arrival of two successive trains on the same path [41,42]. And the dwell time is designed as the headway time in the VCenabledself-organising railway system; thus, the travel cost of each passenger can be estimated as follows:…”

Section: Representation Of Passenger Attributes and Behavior Inmentioning

confidence: 99%

Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique

Zhang

2023

Journal of Advanced Transportation

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Virtual coupling (VC) is a train-centric next generation signalling system, which can enable multiple trains to operate in a formation just like one train or decouple separately on-the-run or at station flexibly or as planned. With the aim of optimizing the interdeparture train headway time, providing the variable capacity for diverse passenger demand, maximizing the passenger riding comfort degree, and minimizing passenger travel cost and train operation cost, the dynamic schedule for VC-enabled rail transit services is investigated with the multiagent simulation technique on NetLogo platform. Our contribution is mainly fourfold. First, VC-enabled rail transit entity for simulation is represented with the multiagent technique, including representation of train unit, train convoy, passenger attributes and behavior, and mathematical formula for calculation of the train operation cost and passenger travel cost, as well as passengers riding comfort degree are proposed. Second, the operational principles for flexible and self-organisingVC-enabled trains are defined. Third, the VC-enabled train-centric, passenger demand-driven, and agent-based simulation flow and algorithms are developed innovatively, which adopt the ergodic strategy for simulation by traversing each O-D pair demand along each route section over the rail transit network. Finally, we test and discuss the proposed methodology on the designed computational experiment on the NetLogo platform, and the simulation results series validated the effectiveness of the proposed methodology. The provided research can effectively support the VC-enabled platoon operation-oriented train service schedule for future study.

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“…Constraint 1: Equation ( 4) depicts that h is constrained by a minimum and maximum service headway. Literature consensus that for a low-demand route characterized by lowfrequency service, the minimum headway can be set to 10 minutes [24]. The minimum desired level of service is set through policy headway, hp, and the vehicle capacity, C governs the maximum value.…”

Section: Constraintsmentioning

confidence: 99%

“…The product of passenger value of time, c2 ($/pass-hr), and equivalent time components Ta (hr), Tw (hr), and Tv (hr) as given in Equation ( 12) are used to estimate CUC. Estimation of Ta and Tv are based on the microeconomic models for vehicle resource consumption derived by Mohring [27] and the model for Tw estimation is based on vehicle and passenger arrival patterns derived by Ansari Esfeh et al [24].…”

Section: ) User Costmentioning

confidence: 99%

Optimal Design of Integrated Semi-Flexible Transit Services in Low-Demand Conditions

Mishra

Mehran

2023

IEEE Access

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Semi-flexible transit (SFT) is commonly discussed as a cost-effective alternative to serving public transportation users in low-demand conditions. We hypothesize that joint optimization of service headway and slack time per trip for route deviation is essential for designing a schedule for the operation of an integrated SFT that can meet both fixed-route and paratransit demand. An integrated SFT has the potential to lower the cost of transportation for regular transit users (both operators and riders) while redirecting potential paratransit riders to less expensive transit modes; thus, reducing demand for overwhelmed paratransit services operating with limited resources. The optimization problem has three competing objectives: minimizing operator costs, minimizing user costs, and maximizing service benefits. Two state-ofthe-art multi-objective evolutionary algorithms NSGA-II and SMPSO are compared to obtain the most representative deterministic Pareto optimal solution set. This study has three major contributions. First, quantile regression is used to suggest multiple slack time values for a given headway that transit planners can consider when generating a static schedule for SFT operation. Second, relationships derived to analyze cost trade-offs suggest that headway governs operator cost and is negatively correlated, user cost is positively and equally influenced by both variables, and slack time governs service benefit and is positively correlated. Third, sensitivity analysis for an integrated SFT operation reveals that low-capacity minivans and standard vans offer higher vehicle occupancy and cost efficiency, mostly economical for low to medium demand (5-20 pass/hr), low permissible deviation from the fixed route is desirable during peak hours to avoid delays for passengers on-board, and extreme weather conditions dramatically and negatively influence costs. Policy recommendations for integrated SFT implementation include a recommendation for fare structure design addressing service equity through surcharges/discounts, vehicle technology and service booking technology advancements for cost reduction, and fleet mix design through estimation of passenger loading profile. The application of the study methodology is demonstrated for a low-demand bus route in Regina, Canada.

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Waiting time and headway modelling for urban transit systems – a critical review and proposed approach

Cited by 38 publications

References 74 publications

Would You Wait? Bus Choice Behavior Analysis Considering Various Incentives

Would You Wait? Bus Choice Behavior Analysis Considering Various Incentives

Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique

Optimal Design of Integrated Semi-Flexible Transit Services in Low-Demand Conditions

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