Flex-route Transit Research Articles

Effects of COVID-19 on flex route transit utilization: An interrupted time series analysis

The COVID-19 outbreak has exerted serious effects on the transportation system, especially public transport. Flex route transit (FRT), one of public transport, has shown significant advantages in transporting passengers during COVID-19 due to its high flexibility and relatively large cabin spaces. This study aims to estimate the effects of the lockdown and first lockdown ease on FRT utilization through interrupted time series analysis. A survey was conducted in Beijing to collect data regarding the daily FRT utilization of individuals, spanning from December 2019 to May 2020. Results indicated that FRT utilization was immediately reduced during the lockdown, and a decreased trend was observed. After the first lockdown ease was rolled out, FRT utilization showed a rising level and trend. Private car owners showed a lower dependence on FRT than non-car owners. The level of income exerted varying effects in a wide range of periods. FRT utilization among the higher income group was more likely to take effect than the lower income group. The study yields various policy implications for FRT operations during COVID-19 or other pandemics.

Optimising Electric Flex-Route Feeder Transit Service with Dynamic Wireless Charging Technology

The emergence of battery electric buses (BEBs) can alleviate environmental problems caused by tailpipe emissions in transit system. However, the high cost of on-board batteries and range anxiety hinder its further development. Recently, the advent of dynamic wireless power transfer technology (DWPT) has become a potential solution to promote the development of BEBs. Hence, this study focuses on the application of DWPT in flex-route transit system. A mixed integer non-linear model is proposed to simultaneously optimise the bus routing and the selection of corresponding bus types considering the constraints of passengers’ travel time, battery size and bus capacity. The objective is to minimise both transit agency cost and passengers’ travel time cost. A tangible hybrid variable neighbourhood search (HVNS) consisting of simulated annealing (SA) and variable neighbourhood search (VNS) is developed to solve the proposed model efficiently. Compared with GAMS (DICOPT solver) and VNS, the proposed algorithm can considerably improve computational efficiency. The results suggest that the proposed model can effectively determine the BEBs’ routing and bus type for flex-route transit system powered by DWPT through a case study in Xi’an China. A comparative analysis shows the proposed model takes 12.97% less total cost than the alternative model with terminal charging technology (TCT).

Optimal fare and headway for a demand adaptive paired-line hybrid transit system in a rectangular area with elastic demand

Demand adaptive paired-line hybrid transit systems that integrate fixed- and flex-route transit have emerged in the last decade and attracted increasing attention because of their potential to improve accessibility for passengers. To facilitate the operation of such a hybrid transit system, this study develops a model to determine the optimal fare and headways associated with fixed- and flex-route transit along a rectangular corridor. Compared with existing literature, the novelty of this study lies in designing the fare structure while simultaneously considering demand elasticity and passenger behaviour. A continuous approximation modelling approach is employed to derive the agency’s and travellers’ cost components. Using these, a nonlinear programming optimisation model is formulated to minimise the total user cost subject to the agency’s nonnegative revenue constraints and passengers’ route choice behaviour, which is characterised as a path-size logit model. Numerical experiments are performed using a stylised network to examine the properties of the model, in which the solution is obtained by combining a brute force method that enumerates headway and fare combinations and an iterative method that determines equilibrated passenger choices. The results show that as potential demand density increases, fare and headway fluctuate and drop, while the percentage of passengers choosing to ride on flex-route transit increases. In addition, there may be an optimal maximum offset distance, which is defined as the width of the corridor to be covered by the transit system, when the potential demand density is low, leading to minimum user cost and maximum travel demand within the service area.

Research on Dynamic Scheduling and Route Optimization Strategy of Flex-Route Transit Considering Travel Choice Preference of Passenger

Research on Dynamic Scheduling and Route Optimization Strategy of Flex-Route Transit Considering Travel Choice Preference of Passenger

The flex-route transit service routing plan considering heterogeneous requests and time windows

Flex-route transit (FRT) service has become increasingly popular in low-density suburban areas as a shared transportation mode. Improving the service quality of FRT heavily relies on effective routing plans. To end of this, this paper addresses the FRT routing plan problem considering heterogeneous requests and time windows (FRTRPP-HRTW), which is formulated as a mixed-integer programming (MIP) model. The MIP model concerns on the objective of minimizing the average system costs. Due to the NP-hard nature of the considering problem, a niche-based modified ant colony optimization (NM-ACO) algorithm is developed to handle it efficiently for large-scale cases. In NM-ACO, a niche-based pheromone update technology is designed to increase the population diversity. Finally, as a case study, a real-world FRT service is employed to validate the effectiveness of NM-ACO. Computational results demonstrate that the NM-ACO can obtain high-quality solutions with small gap in much shorter time compared with the mature solver (i.e. CPLEX). Furthermore, compared with the well-known MMAS algorithm and a state-of-art ACO-DI algorithm, the NM-ACO expresses higher precision and stronger robustness.

Multimodal Feeder Plan of Setting Flex-Route Transit for Metro Terminal Station in Suburb Considering Real-Time Demand Based on Slack Arrival Strategy

Multimodal Feeder Plan of Setting Flex-Route Transit for Metro Terminal Station in Suburb Considering Real-Time Demand Based on Slack Arrival Strategy

Segmenting and Exemplifying Potential Flex Route Transit Adopters

Market segmentation has been considered an essential tool for evolving technology to examine for extensive adoption. Flex route transit (FRT) is a personalized transportation mode, which is expected to grow in the future. Therefore, FRT adoption has sparked a significant amount of research curiosity. This study endeavors to examine and identify potential FRT adopters considering attitudinal factors and socioeconomic characteristics by a framework of “perceived benefits–attitude–intention.” To this end, the technology acceptance model and theory of planned behavior are utilized to extract six attitudinal factors. The multiple indicators multiple causes model is employed to capture these attitudinal factors. Robust analytical procedures (e.g., clustering, multiple discriminant analysis, and chi-square test) are implemented to determine the number of clusters and validate the results by a survey in Beijing based on attitudinal factors and socioeconomic characteristics. Results show that potential FRT adopters are segmented into three clusters, that is, “conservatives,”“skeptics,” and “enthusiasts.” Attitudinal factors and socioeconomic characteristics for each cluster are compared and analyzed. A series of implications are recommended to policymakers, which might offer pertinent suggestions to encourage FRT adoption.

Examining individuals’ adoption of flex route transit

ABSTRACT This study aims to comprehensively identify the contributing factors affecting the adoption of flex route transit. A survey was conducted in 2019, and a multiple indicators multiple causes (MIMIC) model was utilized to capture individuals’ preferences toward car, fixed route, and flex route transit. Subsequently, the multinomial logit model with attitudinal features is employed to identify the key factors influencing the shift from car or fixed route to flex route transit. Results indicate that several factors contribute to flex route transit adoption, with the top three being car restriction, travel cost, and age groups. Car restriction is positively associated with flex route transit adoption (marginal effects 11.67%). Travel costs (≥1.49 US$) and elderly groups also contribute to an increase in flex route transit adoption, with marginal effects of 9.18% and 8.01%, respectively. These findings provide insightful implications for decision-makers in identifying potential adopters and improving flex route transit adoption.

Dynamic Clustering Meeting Points Strategy to Improve Operational Service Capability of Flex-Route Transit

To improve the operational service capability of flex-route transit under the condition of uncertain and inhomogeneous passenger travel demand, a dynamic meeting points optimization solution is proposed. This solution can set dynamic pickup and drop-off meeting points according to real-time travel demand. The flex-route transit operation mode is described in detail and is divided into a two-stage optimization problem. Mixed integer programming is employed to formulate the problem with a twofold objective—(1) serve as many requests as possible; and (2) minimize the time cost of the accepted passengers. An improved memetic solution algorithm is proposed for the model constructed. Computational results based on real-world cases show that the dynamic clustering meeting points solution breaks the space constraints of the original pickup and drop-off points of passengers and can significantly reduce the proportion of passenger reservation requests rejected without increasing operation cost.

Simulation-based optimization considering energy consumption for assisted station locations to enhance flex-route transit

As an emerging mode of public transit, flex-route transit (FRT) is regarded as the key to reducing energy consumption and promoting sustainable development in low-density suburban cities. However, its performance in cases with unexpectedly high demand levels is usually unsatisfactory. To address this issue, a simulation-based optimization method is proposed for assisted station locations in FRT and to reduce the system costs, including that for energy consumption. First, considering the uncertain travel demands of passengers, this location problem is modelled as a simulation-based optimization (SO) problem based on Monte Carlo simulation (MCS). Then, a multiple metamodel-based efficient global optimization (MMEGO) algorithm is proposed to effectively solve the SO problem. The numerical results demonstrate that the proposed MMEGO algorithm can obtain good results with lower computing cost and stronger robustness than counterpart algorithms (EGO and MISK). Finally, simulation tests of FRT based on data from the operation of 320 buses in Shenzhen city and the related OD data are conducted to test the feasibility of the proposed method. The results show that the proposed optimization method can be used to identify suitable locations for assisted stations and reduce the total system cost by 26.6% compared with that for an FRT system with unoptimized assisted station locations.

A Kriging-based optimization method for meeting point locations to enhance flex-route transit services

As a promising on-demand transportation mode in low-demand areas, flex-route transit, has attracted much attention in the transportation research field. However, unexpectedly high demand levels caused by travel uncertainty impact the reliability and development of flex-route transit services. Although the meeting point strategy can deal with this problem effectively, selecting a location for the meeting points can substantially influence the performance of this strategy. In this study, meeting point location selection is modeled as a simulation-based optimization (SO) problem, and a Kriging-based global optimization method using a Pareto-based multipoint sampling strategy (KGO-PS) is proposed to solve this problem. Through comparison of several typical benchmark functions with other counterparts, the effectiveness of KGO-PS has been verified. Moreover, a real-life flex-route transit service is employed to construct the SO problem, and the optimization results show that the proposed algorithm can improve the performance of flex-route transit services under unexpectedly high demand levels.

Understanding Flex-Route Transit Adoption from a Stage of Change Perspective

Given the potential benefits of flex-route transit for the environment and traffic congestion, many cities have goals for diffusing flex-route transit. However, previous studies have focused on practical experience, strategic planning, and operational planning of flex-route transit. Few studies have identified the determinants of flex-route transit adoption. This study aims to address the effect of demographic characteristics and psychological factors on flex-route transit adoption by employing a stage of change framework. First, the transtheoretical model is applied to divide respondents into five ordered stages (i.e., Precontemplation, Contemplation, Preparation, Action, and Maintenance). Second, a survey was conducted in Beijing, China, using an online platform to obtain the demographic characteristics and measurement items of the respondents in the five ordered stages. Then, a structural equation model is applied to quantify psychological factors. In addition, the partial proportional odds (PPO) model is employed to estimate the best model and identify the determinants of flex-route transit adoption. Finally, an elasticity analysis is conducted to uncover the effect of determinants on flex-route transit adoption in the five ordered stages. The results suggest that the PPO model has the best fit and validity. The model reveals the significant implications of psychological factors and demographic characteristics for transport policy in various stages. These findings show that, regardless of the initial stage, interventions concentrating on psychological factors and demographic characteristics appear to have the potential to transfer the stage of individuals from one to another. To make policies as effective as possible for promoting flex-route transit adoption, the targeted interventions should be complemented in the specific stage.

Understanding the effect of sociodemographic and psychological latent characteristics on flex-route transit acceptance.

Flex-route transit (FRT) has significant advantages in low-demand areas. Existing studies have focused on practical experience, strategic planning, and operational planning. Few studies have addressed the effect of sociodemographic and psychological latent characteristics on the acceptance of FRT. This study aims at exploring the effect of sociodemographic and psychological latent characteristics on FRT acceptance. To finish the goal, a household survey is conducted from April to May 2020 in Nanjing, China. The survey includes sociodemographic characteristics and observed variables of individuals. Firstly, the study extracts six psychological latent characteristics to reflect individuals' attitudes based on previous and mature researches in the field of technology acceptance model (TAM) and theory of planned behavior (TPB). Then, a multiple indicators and multiple causes (MIMIC) is applied to calculate six psychological latent characteristics. Finally, an integrated model, consisting of the MIMIC and a binary logit model (BLM), is applied to match sociodemographic and psychological latent characteristics. The BLM with sociodemographic characteristics is developed as the reference model to compare the effects of psychological latent characteristics. Results show that psychological latent factors play a significant role in estimating the effect on FRT acceptance. Results of the integrated model show that the parameter of car is -0.325, displaying individuals with private cars are more reluctant to use FRT. Therefore, restricting private cars is an effective measure to facilitate FRT. Improving flexibility (0.241) is a significant measure to facilitate FRT. Findings are expected to facilitate decision-making of transport planners and engineers, and therefore enhance the service of the FRT system.

GIS-Based Support System for On-Demand Flexroute Transit Service

Geographic Information Systems (GIS) are a proven resource for public transportation service planning and evaluation. In particular, their spatial analysis and database management capabilities make them well suited for such applications. The primary cost incurred in a GIS application is for the development and maintenance of high-quality spatial databases. Due to this cost, public transportation agencies want to utilize these databases to support a wider array of applications. As desktop GIS software and computer hardware become more powerful, GIS can be used to develop applications for “real-time “ operations. In this research effort, a GIS-based prototype system was developed and tested to support the scheduling and dispatch functions of an on-demand flexroute transit service. The effectiveness of the prototype demonstrates the potential of GIS to support time-critical transit operations.

A Multiobjective Optimization Model for Flexroute Transit Service Design

Flexroute transit (also referred to as route-deviation) is an innovative public transportation approach in which service is provided at fixed stops on a predetermined schedule, while also providing “on-demand” service to customers between the fixed stops. A significant challenge to the implementation of flexroute transit has been the complexity inherent in the design process. This research explored two of the key design parameters available to a transit planner: service zone size (the area between fixed stops where deviations are permitted), and slack time distribution (the method used to distribute among zones the total “slack” time built into the schedule to allow for deviations). It was found that the objectives of maximizing feasible deviations and minimizing unused slack time are conflicting and highly sensitive to the design parameters. To assist in the design process, the article formulates a multiobjective binary optimization model that can be used to develop an efficient frontier—guiding transit planners as they seek to explore design trade-offs.

The Multi-Type Demands Oriented Framework for Flex-Route Transit Design

Flex-route transit is regarded as the feasible solution to provide flexible service for various demands. To improve the service of flex-route transit, this paper proposes a design framework with the input of multi-type demands. Firstly, according to the multi-feature-based classification method, static stations and dynamic stations are divided by hierarchical clustering algorithm based on historical demands. Secondly, in the two-stage planning method, an offline plan is generated by multi-route design model and route-design-oriented genetic algorithm based on the classified stations and the flexible combination of reserved demands and regular travel patterns. Then, an online plan is adjusted by route modification model and greedy algorithm based on the offline plan and real-time demands. Numerical experiments demonstrate the applicability of flex-route transit in the realistic road network and show that flex-route transit can transport demands more effectively and save nearly 40% of cost compared with traditional transit.

Feasibility Study of Transferring Shared Bicycle Users with Commuting Demand to Flex-Route Transit—A Case Study of Nanjing City, China

Shared bicycle users with commuting purposes generally need to take a traditional public transit and then use the shared bicycle to complete the first/last mile transport. While shared bicycle provides convenient travel for travelers, it also brings a series of problems such as disorderly parking and road occupancy. Therefore, exploring the problem of travel mode shift between shared bicycle and public transit is of significance for improving the traffic environment and increasing the sharing rate of public transit. This paper introduces the flex-route transit system and quantitatively analyzes the rationality and feasibility of using flex-route transit to pick up and drop off shared bicycle users with commuting demand from the temporal perspectives. A flex-route transit route design model is established with the objective of minimizing the sum of vehicle driving time cost and passenger time cost, and the time cost models of the shared bicycle commuting system and the flex-route transit system are constructed, compared, and analyzed to explore the feasibility of flex-route transit picking up or dropping off shared bicycle users under different conditions. Through the subsequent sensitivity analysis, the influence of passenger demand density, fixed station spacing, and travel preference attributed to the two systems are analyzed separately. The results demonstrate that the flex-route transit can efficiently complete the picking up or dropping off for shared bicycle users under certain conditions.

Improving flex-route transit services with modular autonomous vehicles

With the advent of modular autonomous vehicles (MAVs), this paper presents a novel operational design for flex-route transit services to reduce operation costs of vehicles and improve the service quality of customers. The regime allows the simultaneous dispatch of a certain amount of MAVs from a bus terminal at a departure time. Each MAV is allowed to visit customers freely outside of checkpoints. Self-adaptive capacity and flexible service mode adapt time- and space-dependent demand characteristics. The presented operational design is formulated as a mixed-integer linear program that is NP-hard. A two-stage solution framework is developed to decompose the proposed mathematical programming cautiously. In the first stage, customized dynamic programming with valid cuts is designed to solve a bus scheduling problem efficiently. In the second stage, an effective and fast heuristic is proposed to solve a variant of the dial-a-ride problem and satisfy the technical requirements for developing on-line applications. Numerical examples and a case study show the effectiveness of the proposed design by comparing the flex-route transit services using traditional vehicles.

Vehicle Routing and Scheduling of Flex-Route Transit under a Dynamic Operating Environment

To improve the reliability, responsiveness, and productivity of the flex-route transit service, this paper investigates the vehicle scheduling and routing problem under a dynamic operating environment. First, we discuss the new operating polices after the introduction of intelligent transportation systems (ITSs), including automatic vehicle location (AVL) system, mobile data terminal (MDT), and computer-aided dispatch (CAD) system. Second, a mixed integer programming (MIP) formulation is employed to solve the offline routing problem. Third, an online scheduling scheme is presented to tackle different dynamic events, such as dynamic requests, travel time fluctuations, cancellations of requests, and customer no-shows. Finally, simulation experiments based on a real-life flex-route transit service are conducted to assess the influence of different dynamic events. The results demonstrate that the proposed scheduling scheme is reliable for coping with various dynamic events, and our findings can be used to guide the policy making of flex-route transit services.

Travelers’ Potential Demand toward Flex-Route Transit: Nanjing, China, Case Study

AbstractAs an innovative combination of conventional fixed-route transit and demand-responsive services, flex-route transit is a promising transit option that can address the travel needs of reside...