Optimization and Technology-Based Strategies to Improve Public Transit Performance Accounting for Demand Distribution

Public transit is important to societies worldwide. The operation of public transit systems is generally associated with great benefits for the users, but there are also cases in which these systems demonstrate inefficient performance. Quantifying transit performance is an important area of research over the last decades. This dissertation presents models to improve transit system performance through optimization techniques and new technologies, recognizing the effects of non-uniform distribution of demand over space and time. The contributions span fixed route transit services and on-demand transit, as well as models for flexible transit operations that lie in between. Regarding fixed route systems, a methodology is proposed to estimate the number of passengers being left-behind subway train vehicles due to overcrowding. Methods to identify appropriate time periods and locations for studying this phenomenon are presented. The effects of overcrowding on passenger waiting times are also investigated. The challenging case of transit networks where passengers tap-in only upon entrance is analyzed, adding a new methodology to a very short list of similar studies and enhancing previous work in this field. For demand responsive systems, this dissertation focuses on optimizing the operation of paratransit services through coordination with alternative providers in order to decrease high operating costs of such a service. The analysis includes a heuristic-based method. The proposed model is more detailed than existing aggregated methods and is able to perform well in high demand levels, unlike existing exact approaches. This part of the dissertation also assists in making transportation network companies a complementary part of public transit, rather than a competitor. Finally, flexible transit systems are studied to identify the operational and demand related characteristics of a service area that could serve as indicators of such systems\u27 efficient performance. The focus here is on route deviation flexible services. Continuous approximation is used to model this flexible system. A new optimized hybrid transit system with elements of both fixed route and flexible services is proposed. Finally, it is highlighted that the current COVID-19 pandemic has proven the need for public transit systems that could be adjusted to accommodate changes in transit demand

Identifying shifts in multi-modal travel patterns during special events using mobile data: Celebrating Vappu in Helsinki

Large urban special events significantly contribute to a city's vibrancy and economic growth but concurrently impose challenges on transportation systems due to alterations in mobility patterns. This study aims to shed light on mobility patterns by utilizing a unique, comprehensive dataset collected from the Helsinki public transport mobile application and Bluetooth beacons. Earlier methods, relying on mobile phone records or focusing on single traffic modes, do not fully grasp the intricacies of travel behavior during such events. We focus on the Vappu festivities (May 1st) in the Helsinki Metropolitan Area, a national holiday characterized by mass gatherings and outdoor activities. We examine and compare multi-modal mobility patterns during the event with those during typical non-working days in May 2022. Through this case study, we find that people tend to favor public transport over private cars and are prepared to walk longer distances to participate in the event. The study underscores the value of using comprehensive multi-modal data to better understand and manage transportation during large-scale events.Comment: 6 pages, 12 figures, Submitted to ITSC202

Measuring Left-Behinds on Subway

ISA# 92312Publication Date from Cover: May 2018The Massachusetts Bay Transportation Authority (MBTA) uses performance measures to monitor its service and measure improvement. This project supports the development of measures that track the customer experience instead of the performance of vehicles. Current measures are based on fare card records and assume that passengers are able to get on the first available vehicle that arrives at a stop or station. There is not currently a way to measure people left behind on subway platforms when vehicles are too full to board. This report presents the development of methods to measure or estimate the number of passengers that are left behind when vehicles are too crowded to board and the distribution of waiting times experienced by passengers, accounting for left-behind passengers. In addition to making use of existing vehicle location data, the study includes evaluation of two potential technologies for measuring passengers: automated passenger counting from surveillance video feeds, and tracking of Media Access Control (MAC) addresses from Bluetooth and Wi-Fi-enabled wireless devices. The occurrence of at least one passenger being left behind can be estimated with 90% accuracy, and the total number left-behind passengers during a rush period can be estimated within 10%. Challenges and opportunities for the future are identified

Continuous Approximation Model for Hybrid Flexible Transit Systems with Low Demand Density

Flexible transit systems are a way to address challenges associated with conventional fixed route and fully demand responsive systems. Existing studies indicate that such systems are often planned and designed without established guidelines, and optimization techniques are rarely implemented on actual flexible systems. This study presents a hybrid transit system where the degree of flexibility can vary from a fixed route service (with no flexibility) to a fully flexible transit system. Such a system is expected to be beneficial in areas where the best transit solution lies between the fixed route and fully flexible systems. Continuous approximation techniques are implemented to model and optimize the stop spacing on a fixed route corridor, as well as the boundaries of the flexible region in a corridor. Both user and agency costs are considered in the optimization process. A numerical analysis compares various service areas and demand densities using input variables with magnitudes similar to those of real-world case studies. Sensitivity analysis is performed for service headway, percent of demand served curb-to-curb, and user and agency cost weights in the optimization process. The analytical models are evaluated through simulations. The hybrid system proposed here achieves estimated user benefits of up to 35% when compared with fixed route systems, under different case scenarios. Flexible systems are particularly beneficial for serving corridors with low or uncertain demand. This provides value for corridors with low demand density as well as communities in which transit ridership has dropped significantly because of the COVID-19 pandemic

Mixed fleets of automated and human-driven vehicles in public transport systems : An evaluation of feeder line services

| openaire: EC/H2020/856602/EU//FINEST TWINS Funding Information: This research was partially funded by the FinEst Twins Center of Excellence (H2020 Grant 856602 ). Publisher Copyright: © 2023 The Author(s)This study focuses on the transitioning period of operating mixed fleets of both automated and human-driven vehicles for public transit services. The type of service investigated here is flexible, including elements of both fixed route and on-demand systems. The operation of the mixed fleet is optimized with analytical methods leading to models for optimal service headway and stop spacing for the two types of vehicles. Analytical models for optimal passenger capacity per vehicle and required fleet size for each type of vehicles are also derived. Four operational strategies are considered, referring to whether the two types of vehicles operate jointly or independently in terms of optimal service headway and stop spacing within the mixed fleet. Numerical analyses indicate that automated vehicles operate optimally with less frequent vehicle dispatches and more fixed stop locations compared to human-driven vehicles. They also require greater fleet size and similar passenger capacity per vehicle. The four operational strategies perform similarly in terms of total generalized costs for the input values considered here. However, sensitivity analyses showed that the operational characteristics of the two types of vehicles in a mixed fleet and the performance of the four operational strategies depend significantly on the percentage of total demand that each type of vehicle serves, as well as on the automated vehicles’ speed and in-vehicle travel time cost for users. The mixed fleets represent the transitioning period towards transit fleets of automated vehicles only and it is shown to be the costliest period for both users and operators.Peer reviewe

Optimal matching for coexisting ride-hailing and ridesharing services considering pricing fairness and user choices

| openaire: EC/H2020/856602/EU//FINEST TWINSMobility-on-demand (MoD) has the potential to revolutionise the patterns of urban mobility. Typically, an MoD platform provides both ride-hailing and ridesharing services, exacerbating the challenges of operating a city-scale real-time MoD system. Existing studies assume that travellers are fully compliant with the platform’s decisions regarding pricing and vehicle assignments, whereas, in reality, travellers can choose different modes based on monetary costs and travel experience, which may conflict with the results derived from the system perspective. In this study, we relax this assumption by accounting for pricing fairness and the travellers’ modal choices within a framework designed to optimise vehicle–traveller matching when both ride-hailing and ridesharing services are provided by an MoD platform. Six fairness principles are defined to characterise fair pricing for shared rides. Computationally efficient optimisation problems are formulated accounting for co-existing ride-hailing and ridesharing services. In numerical experiments, we assess the effectiveness of our method and compare it with state-of-the-art ones using a dataset of taxi requests for New York City. The results show that our optimisation strategy can significantly increase the service ratio and profit without sacrificing the service quality.Peer reviewe

Estimation of left behind subway passengers through archived data and video image processing

Crowding is one of the most common problems for public transportation systems worldwide, and extreme crowding can lead to passengers being left behind when they are unable to board the first arriving bus or train. This paper combines existing data sources with an emerging technology for object detection to estimate the number of passengers that are left behind on subway platforms. The methodology proposed in this study has been developed and applied to the subway in Boston, Massachusetts. Trains are not currently equipped with automated passenger counters, and farecard data is only collected on entry to the system. An analysis of crowding from inferred origin–destination data was used to identify stations with high likelihood of passengers being left behind during peak hours. Results from North Station during afternoon peak hours are presented here. Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. Automatically counted passengers and train operations data were used to develop logistic regression models that were calibrated to manual counts of left behind passengers on a typical weekday with normal operating conditions. The models were validated against manual counts of left behind passengers on a separate day with normal operations. The results show that by fusing passenger counts from video with train operations data, the number of passengers left behind during a day’s rush period can be estimated within 10% of their actual number

Real-time, on-board crowding estimation in public transport networks with multiple lines using non-exhaustive Automatic Passenger Counting data

Accurate information about passenger volumes and flows in public transport is important for the efficient operation, management, and evaluation of the network. Passengers’ comfort of travel is a major criterion for choosing public transport against less sustainable modes and the prevention of crowding inside vehicles is a challenging task for managers and operators of public transport services. The avoidance of crowds became even more critical during COVID-19, which highlighted the need for preparedness in terms of a proper provision of information on crowding phenomena. In recent years, information about passenger volume on-board public transport vehicles is commonly derived from Automatic Passenger Count data. Such data are often incomplete and there is a critical need for methods to estimate the missing records. An existing study developed a Kalman filter-based scheme for estimating the number of passengers on-board public transport vehicles, employing non-exhaustive real-time Automatic Passenger Counting data. The current study builds upon this study and extends it in order to allow estimations for networks with multiple common lines per station. The accuracy and reliability of the estimation are evaluated through application to the commuter train network of Helsinki, Finland, and the results suggest that the proposed method is able to deliver good estimation accuracy in terms of the number of passengers boarding, alighting, and, ultimately, comfort Levels of Service