The multi-vehicle dial-a-ride problem with interchange and perceived passenger travel times

The Dial-a-Ride Problem (DARP) introduced in the early 1980s is the NP-Hard optimization problem of developing the most cost-efficient vehicle schedules for a number of available vehicles that have to start from a depot, pick up and deliver a set of passengers, and return back to the same depot. DARP has been used in many modern applications, including the scheduling of demand-responsive transit and car pooling. This study departs from the original definition of DARP and it extends it by considering an interchange point where vehicles can exchange their picked-up passengers with other vehicles in order to shorten their delivery routes and reduce their running times. In addition to that, this study introduces the concept of generalized passenger travel times in the DARP formulation which translates the increased in-vehicle crowdedness to increased perceived passenger travel times. This addresses a key issue because the perceived in-vehicle travel times of passengers might increase when the vehicle becomes more crowded (i.e., passengers might feel that their travel time is higher when they are not able to find a seat or they are too close to each other increasing the risk of virus transmission or accidents). Given these considerations, this study introduces the Dial-a-Ride Problem with interchange and perceived travel times (DARPi) and models it as a nonlinear programming problem. DARPi is then reformulated to a MILP with the use of linearizations and its search space is tightened with the addition of valid inequalities that are employed when solving the problem to global optimality with Branch-and-Cut. For large problem instances, this study introduces a tabu search-based metaheuristic and performs experiments in benchmark instances used in past literature demonstrating the computation times and solution stability of our approach. The effect of the perceived passenger travel times to the vehicle running costs is also explored in extensive numerical experiments.</p

A model for modifying the public transport service patterns to account for the imposed COVID-19 capacity

As public transport operators try to resume their services, they have to operate under reduced capacities due to COVID-19. Because demand can exceed capacity at different areas and across different times of the day, drivers have to refuse passenger boardings at specific stops to avoid overcrowding. Given the urgent need to develop decision support tools that can prevent the overcrowding of vehicles, this study introduces a dynamic integer nonlinear program to derive the optimal service patterns of individual vehicles that are ready to be dispatched. In addition to the objective of satisfying the imposed vehicle capacity due to COVID-19, the proposed service pattern model accounts for the waiting time of passengers. Our model is tested in a bus line connecting the University of Twente with its surrounding cities demonstrating the trade-off between the reduced in-vehicle crowding levels and the excessive waiting times of unserved passengers

A model for the periodic optimization of bus dispatching times

We model the problem of dispatching time control in rolling horizons following a periodic optimization approach reactionary to travel time and passenger demand disturbances. This model provides more flexibility to transport planners allowing them to adjust the bus schedules during the daily operations. We prove that our periodic optimization model is a convex quadratic program, guaranteeing the global optimality of its solution. To reduce the computational burden, we introduce an iterative algorithm that uses gradient approximations to obtain an approximate dispatching solution. The proposed solution method is found to be significantly faster than exact optimization approaches for quadratic programming and maintains an (almost) negligible optimality gap in realistic bus operation scenarios. Finally, we show that our periodic optimization method outperforms myopic methods that adjust the dispatching time of each bus trip in isolation using operational data from bus line 302 in Singapore

Coordinating feeder and collector public transit lines for efficient MaaS services

Coordinating the schedules of feeder and collector public transit lines can reduce the passenger travel times. With advances in smart mobility, mobility-as-a-service (MaaS) schemes allow passengers to book a combined ticket for all their trip legs. This detailed information about the origins and destinations of door-to-door trips offers the opportunity to coordinate the schedules of public transport lines to reduce the passenger travel times. In this study, we model the coordination problem of feeder and collector lines by explicitly considering the regularity of the feeder lines and the transfer times of passengers. The coordination problem is modeled as a nonlinear non-convex problem and redormulated to an casy-to-solve convex optimization problem. Because of the travel times uncertainty, we also introduce a stochastic optimization formulation based on the sample average approximation approach. We test the performance of our approach in a case study with feeder and collector lines in Singapore showing an improvement potential of 5-10% in passenger travel times

Robust timetable optimization for bus lines subject to resource and regulatory constraints

Timetables are typically generated based on passenger demand and travel time expectations. This work incorporates the travel time and passenger demand uncertainty to generate robust timetables that minimize the possible loss at worst-case scenarios. We solve the resulting minimax problem with a genetic algorithm that uses sequential quadratic programming to evaluate the worst-case performance of each population member. Our approach is tested on a bus line in Singapore demonstrating an improvement potential of ≃5% on service regularity and excessive trip travel times

Robust timetable optimization for bus lines subject to resource and regulatory constraints

Timetables are typically generated based on passenger demand and travel time expectations. This work incorporates the travel time and passenger demand uncertainty to generate robust timetables that minimize the possible loss at worst-case scenarios. We solve the resulting minimax problem with a genetic algorithm that uses sequential quadratic programming to evaluate the worst-case performance of each population member. Our approach is tested on a bus line in Singapore demonstrating an improvement potential of ≃5% on service regularity and excessive trip travel times

At-stop control measures in public transport: Literature review and research agenda

In this literature review, we systematically review studies on public transit control with a specific focus on at-stop measures. In our synthesis of the relevant literature, we consider three perspectives: (1) the mathematical models of the proposed methodologies; (2) their complexity; (3) their applicability in real-time operations and their advantages and disadvantages considering their practical implications. The reviewed control methods include holding, dynamic dispatching, and stop-skipping. Control methods, that have attracted more attention in recent years due to the advancements in automation and data availability, aim at alleviating the negative effects of service variability because of external disruptions. Following the synthesis of the literature, we propose a research agenda pertaining to the combination of control measures, passenger-oriented decision making, coordinated network control, deployment of electric buses and disturbance management.</p

Integration of shared transport at a public transport stop: the role of digital skills in mode choice intentions

Questionnaire of a survey with 710 respondents in Leyenburg, The Hague, The NetherlandsTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

GTFS-RT data from bus line 15L in Denver. Date: Saturday, July 22, 2017

GTFS-RT data from bus line 15L in Denver. Date: Saturday, July 22, 2017. 61 bus trips. It includes the actual arrival times of all the daily trips (61 in total) at each bus stop. An interpolation has been performed in some sporadic cases of missing data entries