Demand Responsive Feeder Transit Research Articles

Static and Dynamic Scheduling Method of Demand-Responsive Feeder Transit for High-Speed Railway Hub Area

Static and Dynamic Scheduling Method of Demand-Responsive Feeder Transit for High-Speed Railway Hub Area

Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line

Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.

Coordinated Operation of Fixed-Route and Demand-Responsive Feeder Transit Services in a Travel Corridor

With the wide use of smartphone travel apps, a new hybrid transit system (HTS) that integrates fixed-route transit (FRT) and app-based demand-responsive feeder transit (DRFT) services have recently emerged in many cities over the world. App-based DRFT services are coordinated with FRT to offer fast, comfortable, and well-connected mobility services to travelers. The study investigates a new variant of HTS, termed flexible HTS (FHTS), allowing FRT to have flexible vehicle departure times and capacitated vehicle loads and DRFT to have flexible feeder stations, so as to further enhance the coordination between FRT and DRFT. The coordinated operation optimization problem of such an FHTS in a travel corridor is formulated as a mixed-integer linear programming model. A novel hybrid metaheuristic algorithm is developed to solve it. In the hybrid algorithm, a constrained compass search method is used to optimize FRT vehicle departure times, and an adaptive large neighborhood search algorithm, coupled with a tailored forward time slack algorithm, is applied to solve the DRFT routing and scheduling problems. A case study in Shucheng County, China is conducted to examine the performance of the model and solution algorithms. Sensitivity analyses on feeder station layout and vehicle size are also conducted. The results show that flexible FRT vehicle departure times and flexible DRFT feeder stations have a great potential of enhancing the coordination of FRT and DRFT services by reducing operators’ costs and passengers’ total travel time. Managerial implications are to set feeder transit stations downstream of an FRT line and to keep a reasonable station spacing.

Developing an optimal algorithm for demand responsive feeder transit service accommodating temporary stops

Demand responsive feeder transit can minimize passengers’ travel times and operator’s costs by optimizing routing based on real demand. One question about the demand response feeder transit operation is whether it can be optimized with door-to-door service or with temporary stops for picking up and delivering passengers. Obviously, door-to-door service eliminates passengers’ walking distances, but it increases passenger in-vehicle travel times and vehicle operating distance and costs. On the other hand, demand responsive feeder transit with temporary stops, which designates the temporary locations picking up and dropping passengers, minimizes bus operating distance and time as well as passenger in-vehicle times, although it increases passengers’ walking distances and times. The developed model uses metaheuristic approaches, including two main algorithms; a passenger’s clustering algorithm based on Particle swarm optimization (PSO) approach and a vehicle routing algorithm that uses simulated annealing (SA) solving method. The algorithm developed an optimal algorithm for clustering and grouping of passengers considering their physical locations and time windows then it was integrated with the authors’ previously developed algorithm for the optimal flexible feeder bus routing as a mixed integer model that objects to minimize the total costs including both passengers traveling times and operator’s operating costs The results of this study showed that although feeder networks with temporary stops always lower operating costs and lessen in-vehicle travel time compared to those with a door-to-door option, the total costs and optimal routings are highly sensitive to the location of passengers.

Impact of Automated Vehicles on Optimal Demand-Responsive Feeder Transit Network Design

This article aims to examine the economic benefits of automating flexible demand responsive feeder transit systems using a developed feeder bus routing optimization algorithm. The objective function of the algorithm is to minimize total passengers' and operating costs of the system. The results showed that when unit operating costs decline, total operating costs, and total costs obviously decline. Furthermore, when unit operating costs decline, the average passenger travel distance and total passenger travel costs decline while the ratio of total operating costs per unit operating costs increases. That means if unit operating costs decrease, the portion of passenger travel costs in the total costs increases, and the optimization process tends to reduce passenger costs more while reducing total costs. Assuming that automation of the vehicles reduces the operating costs, it will reduce not only total operating costs and total costs, but also total passenger travel costs.

Joint Optimization of Running Route and Scheduling for the Mixed Demand Responsive Feeder Transit With Time-Dependent Travel Times

As an emerging urban public transport mode, responsive feeder transit system is flexible and can offer door-to-door services between new districts at margins with low urban transit coverage and trunk bus station. In this study, a joint optimization of running route and scheduling for responsive feeder transit under mixed demand (i.e., reservation and real-time demands) of the time-dependent road network was investigated. A two-stage optimization method was designed together with considering the mixed demands. At the first stage, the initial running route and scheduling were determined according to all reservation demands. At the second stage, the running route and scheduling were continuously optimized based on the real-time demands. The real-time demand responsive strategy, which is built up by using quantitative batch treatment rather than immediate treatment and dynamic route updating strategy for global optimization, were designed by utilizing the submission order of real-time demands. A joint optimization model of running route and scheduling was constructed based on the quantitative batch decision points in the time-dependent road network together with combination of the actual road network. In this model, the minimum total system cost was used, which is composed of the vehicle running costs and passengers' traveling time costs with constraints including vehicle capacity, passengers' time window, and vehicle running time. A solving algorithm based on the adaptive genetic algorithm was designed by considering the characteristics of the joint optimization model.

Two-Step Coordinated Optimization Model of Mixed Demand Responsive Feeder Transit

AbstractIt is necessary to consider both the reservation and real-time travel demand in order to coordinate and optimize vehicle routing and scheduling. In this paper, a two-step coordinated optimi...

An Optimization Model for Demand-Responsive Feeder Transit Services Based on Ride-Sharing Car

Ride-sharing (RS) plays an important role in saving energy and alleviating traffic pressure. The vehicles in the demand-responsive feeder transit services (DRT) are generally not ride-sharing cars. Therefore, we proposed an optimal DRT model based on the ride-sharing car, which aimed at assigning a set of vehicles, starting at origin locations and ending at destination locations with their service time windows, to transport passengers of all demand points to the transportation hub (i.e., railway, metro, airport, etc.). The proposed model offered an integrated operation of pedestrian guidance (from unvisited demand points to visited ones) and transit routing (from visited ones to the transportation hub). The objective was to simultaneously minimize weighted passenger walking and riding time. A two-stage heuristic algorithm based on a genetic algorithm (GA) was adopted to solve the problem. The methodology was tested with a case study in Chongqing City, China. The results showed that the model could select optimal pick-up locations and also determine the best pedestrian and route plan. Validation and analysis were also carried out to assess the effect of maximum walking distance and the number of share cars on the model performance, and the difference in quality between the heuristic and optimal solution was also compared.

Solving demand-responsive feeder transit service design with fuzzy travel demand: A collaborative ant colony algorithm approach

Solving demand-responsive feeder transit service design with fuzzy travel demand: A collaborative ant colony algorithm approach

Personalised and Coordinated Demand-Responsive Feeder Transit Service Design: A Genetic Algorithms Approach

The purpose of this work is to create an efficient optimization framework for demand-responsive feeder transit services to assign vehicles to cover all pickup locations to transport passengers to a rail station. The proposed methodology features passengers placing a personalized travel order involving the subway schedule chosen by passengers and windows of service time, etc. Moreover, synchronous transfer between the shuttle and feeder bus is fully accounted for in the problem. A mixed-integer linear programming model is formulated to minimize the total travel time for all passengers, which consists of ride-time for vehicles from the pickup locations to the rail station and wait-time for passengers taking the subway beforehand. Different from conventional methods, the proposed model benefits from using a real distribution of passenger demand aggregated from cellular data and travel time or the distance matrix obtained from an open GIS tool. A distributed genetic algorithm is further designed to obtain meta-optimal solutions in a reasonable amount of time. When applied to design a feeder bus system in Nanjing City, China, case study results reveal that the total travel time of the proposed model was reduced by 2.46% compared to the traditional model. Sensitivity analyses were also further performed to investigate the impact of the number of vehicles on the output. Finally, the difference in results of Cplex, standard GA, and the proposed algorithm were compared to prove the validity of the algorithm.

Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

This paper presents a mixed-integer linear programming model for demand-responsive feeder transit services to assign vehicles located at different depots to pick up passengers at the demand points and transport them to the rail station. The proposed model features passengers’ one or several preferred time windows for boarding vehicles at the demand point and their expected ride time. Moreover, passenger satisfaction that was related only to expected ride time is fully accounted for in the model. The objective is to simultaneously minimize the operation costs of total mileage and maximize passenger satisfaction. As the problem is an extension of the nondeterministic polynomial problem with integration of the vehicle route problem, this study further develops an improved bat algorithm to yield meta-optimal solutions for the model in a reasonable amount of time. When this was applied to a case study in Nanjing City, China, the mileage and satisfaction of the proposed model were reduced by 1.4 km and increased by 7.1%, respectively, compared with the traditional model. Sensitivity analyses were also performed to investigate the impact of the number of designed bus routes and weights of objective functions on the model performance. Finally, a comparison of Cplex, standard bat algorithm, and group search optimizer is analyzed to verify the validity of the proposed algorithm.

Critical street links for demand responsive feeder transit services

This paper presents a methodology to identify and locate critical links in a grid street network system for feeder transit services. A ‘critical’ link can be defined as a link that—when eliminated from or appended to an existing network—would cause the largest change in the network connectivity and consequently in the transit performance. The most significant contribution of this study is to present a simple analytical approach to locating the critical link(s) for a grid street network system of any size with uniform passenger demand across the service area. The distance between demands points have been used as the basic measure of impedance in the final derived closed-form equations. Easily computable formulas for identifying the critical links have been validated by simulation analyses with the street network system from the City of St. Joseph, Missouri. Useful insights from the analytical derivations and simulation results indicate a monotonic decrease in link criticality when moving from the centrally located links to those located at the periphery in a grid street network system.

A model for estimating the optimal cycle length of demand responsive feeder transit services

The general lack of first/last mile connectivity is one of the main challenges faced by today’s public transit. One of the possible actions towards a solution to this problem is the planning, design and implementation of efficient feeder transit services. This paper develops an analytical model which allows for an easy computation of near optimal terminal-to-terminal cycle length of a demand responsive feeder service to maximize service quality provided to customers, defined as the inverse of a weighted sum of waiting and riding times. The model estimates the recommended cycle length by only plugging in geometrical parameters and demand data, without relying on extensive simulation analyses or rule of thumbs. Simulation experiments and comparisons with real services validate our model, which would allow planners, decision makers and practitioners to quickly identify the best feeder transit operating design of any given residential area.

A new street connectivity indicator to predict performance for feeder transit services

This paper defines a novel street Connectivity Indicator (C.I.) to predict transit performance by identifying the role that street network connectivity plays in influencing the service quality of demand responsive feeder transit services. This new C.I. definition is dependent upon the expected shortest path between any two nodes in the network, includes spatial features and transit demand distribution information and is easy to calculate for any given service area. Simulation analyses over a range of networks have been conducted to validate the new definition. Results show a desirable monotonic relationship between transit performance and the proposed C.I., whose values are directly proportional and therefore good predictors of the transit performance, outperforming other available indicators, typically used by planners.

Comparing Fixed-Route and Demand-Responsive Feeder Transit Systems in Real-World Settings

This research presents a method for comparing fixed-route transportation systems and demand-responsive feeder transit systems by using passenger survey data, published transit schedules, and optimal routing techniques. Demand-responsive transportation can be used to improve transit service levels in low-demand areas. Because demand in cities can vary significantly across the region and by time of day, an effective means of determining when demand-responsive services can outperform fixed-route services, and vice versa, is needed. This research builds on existing comparison techniques that focus on gridded street systems and expands the techniques to include all types of street networks, transit schedules, and passenger demand levels. The generic techniques are presented and a case study is given for Atlanta, Georgia, to determine where demand-responsive feeder systems could be implemented to improve customer satisfaction and reduce operating costs.

Optimal Zone Design for Feeder Transit Services

Feeder transit services generally operate within residential service areas and move customers to and from a transfer point that connects to a major fixed-route transit network. Feeders can operate in a traditional fixed-route or in an emerging demand-responsive fashion. In designing such systems, planners may divide the entire service area into zones independently served by a single feeder line to provide better customer service, lower operating cost, and make management of the operations easier. An analytical model is developed to help decision makers determine the number of zones in a residential service area while balancing customer service quality and vehicle operating costs. For fixed-route and demand-responsive feeder transit, closed-form expressions and numerical procedures are used to derive the optimal number of zones as a function of the main parameters. Analytical expressions are validated by simulation runs.