Demand Responsive Transit Service Research Articles

An Optimal Road Network Extraction Methodology for an Autonomous Driving-Based Demand-Responsive Transit Service Considering Operational Design Domains

In addition to addressing the labor shortage due to an aging population, the transition to autonomous vehicle (AV)-based mobility services offers enhanced efficiency and operational flexibility for public transportation. However, much of the existing focus has been on improving AV safety without fully considering road conditions and real-world service demand. This study contributes to the literature by proposing a comprehensive framework for efficiently integrating AV-based mobility services at the network level, addressing these gaps. The framework analyzes and optimizes service networks by incorporating actual demand patterns, quantifying road segment difficulty from an AV perspective, and developing an optimization model based on these factors. The framework begins by quantifying the operational difficulty of road segments through an evaluation of Operational Design Domains (ODDs), providing a precise measure of AV suitability under varying road conditions. It then introduces a quantitative metric to assess operational feasibility, considering factors such as the service margin, costs, and safety risks. Using these metrics alongside Genetic Algorithms (GAs), the framework identifies an optimal service network that balances safety, efficiency, and profitability. By analyzing real-world data from different mobility services, such as taxis, Demand-Responsive Transport (DRT), and Special Transportation Services (STSs), this study highlights the need for service-specific strategies to optimize AV deployment. The findings show that optimal networks vary with demand patterns and road difficulty, demonstrating the importance of tailored network designs. This research provides a scalable, data-driven approach for integrating AV services into public transportation systems and lays the foundation for further improvements by incorporating dynamic factors and broader urban contexts.

The public transport disadvantaged in a highly transit-oriented city: An analytical framework, key challenges and opportunities

This paper proposes a holistic framework to identify public transport disadvantaged areas in terms of the level of services (LOS), area-based accessibility, and facility-based accessibility by employing the General Transit Feed Specification (GTFS) dataset. The framework considers the spatial and temporal levels of transit services, the spatial separation by urban functional areas, the availability of facilities based on the cumulative opportunity method, and the minimum time cost based on dual accessibility. A case study is performed in Hong Kong. The results show that the main problem for rural residents using public transport services lies in walking to transit stops/piers instead of long waits at public transport stops or infrequent services. The map of area-based accessibility shows great variations by urban functional areas. Despite being a highly transit-dependent city, we find huge disparities of accessibility to public facilities within Hong Kong and people living in some rural areas have difficulties in accessing basic services such as education, health care and shopping through public transport. Theoretically, this study reinforces the significance of place-based transport disadvantage and its intricate relationship with urban form. Empirically, it provides practical recommendations for public transport planning. The findings can direct transit agencies to re-plan and deploy services such as introducing demand-responsive transit services to better address the needs of people living in transport disadvantaged areas.

Demand-Responsive Transit Service With Soft Time Windows Considering Real-Time Disruptions Based on Bounded Rationality

Demand-responsive transit (DRT) with smartphone-based applications is emerging as a flexible and sustainable mobility service, transforming urban transportation. Nevertheless, to satisfy the real-time and inconsistent demand, it is becoming increasingly important to capture the decision-making psychology of order cancellations. In this study, a two-phase optimization framework is presented in response to real-time disruptions, including order cancellations and the insertion of new real-time passengers. In contrast to random real-time demand, this paper is more concerned about the impacts of the feedback information on order cancellations. Bounded rationality is incorporated into the model to discuss the decision-making process of cancellation behaviors. With regard to the soft window, a compensation strategy is proposed to promote the profit while encouraging passengers for a long-term use. Additionally, solution algorithm based on variable neighborhood search (VNS) and rolling horizon is constructed to approach the Pareto solutions set. To testify the validity of the proposed algorithm, small-scale experiments in simplified Sioux Falls network are investigated for multiple runs. Meanwhile, a real-world case study in Beijing is explored to evaluate the system performance considering real-time disruptions. The results indicate that the dynamic DRT service can substantially improve the system profit but increase the penalty cost. The profit presents a significant improvement to 940 (renminbi) RMB as a result of the insert of real-time passengers. This study, therefore, not only provides a deeper insight into the analysis of passenger cancellation behavior but also contributes to construct a more flexible DRT service.

Assessing the sustainability of time-dependent electric demand responsive transit service through deep reinforcement learning

Demand responsive transit (DRT) is expected to offer enormous possibilities for fulfilling the ever-growing diversified demand while promoting the urban sustainability. Nevertheless, it seems to remain critical but intractable to make the routing decision in response to the time-dependent travel speeds. Considering the risk of speed uncertainty, deep reinforcement learning (DRL) algorithm is presented to address the time-dependent electric DRT problem in this study. Long short-term memory (LSTM) is integrated into the attention mechanism at the decoding process. To evaluate the sustainability, the reward function can be subdivided into the number of served passengers, regret utility, and carbon emissions. To testify the effectiveness, we made a comparison between the CPLEX solver, NSGA-II, and the proposed algorithm in a realistic transportation network of Beijing. The computational results demonstrate that DRL algorithm with shorter computation time and better solutions is dramatically superior to the other approaches. Therefore, the DRL algorithm provides a more efficient framework for addressing the time-dependent electric DRT problem while improving the sustainability of the environment and society.

Improving Demand Responsive Transit Services: Insights from the London Field Test

Improving Demand Responsive Transit Services: Insights from the London Field Test

Assessing the introduction of regional driverless demand-responsive transit services through agent-based modeling and simulation

AbstractDemand-responsive transit systems are seen as a solution to improve the level of service and decrease the costs of providing transit to rural or low-demand areas. There are great expectations on the impact that driverless demand-responsive transit (DDRT) systems can have on a regional setting. Nevertheless, the application of DDRT in such type of setting has not been widely studied, in particular for scenarios where the system is an addition to the existing alternatives (as characteristic of the early diffusion stage of an innovation). This study provides an assessment of the early-stage operation of a regional DDRT system used by a small portion of the entire potential adopters. A methodological approach is built upon an agent-based model through which the daily operation of a DDRT system is simulated in detail. This approach is applied to a case study in Portugal considering scenarios varying in terms of several operational parameters, adoption rates (from 1 to 20%), and types of services (private versus shared rides). Results show that each vehicle in the DDRT system with shared rides could replace up to 13 private vehicles with a decrease in the vehicle-kilometers traveled of 1%. The DDRT system operation is profitable even with low adoption rates (starting from 2% for the system with shared rides). When considering the adoption rate of 5%, the system with shared rides is 54% more profitable than the one with private rides, but, from the perspective of travelers, shared rides lead to longer waiting times and detours.

Designing Mobility Policies for Vulnerable Users Employing the Living Lab Approach: Cases of a Demand-Responsive Transit Service in Ljubljana and Maribor

In recent years, transport policy has strongly been oriented to develop more inclusive cities and to design mobility services aligned to specific users’ needs. In an attempt to guarantee an adequate level of accessibility, especially to vulnerable users, approaches based on the dialogue between public administration, operators, citizens and researchers (so-called “quadruple helix”) have been proposed. The two case studies presented in this article refer to the development of a minibus demand-responsive transport system, devoted mainly to people with reduced mobility, in the cities of Ljubljana and Maribor (Slovenia) designed using a Living Lab participative approach. In fact, urban mobility stakeholders were engaged in this process during the design and monitoring phases of the pilot projects. Their involvement resulted in producing positive outcomes: citizens actively participated in the projects, and the designed service was perceived to be useful and effective. Both projects were successful, as statistics demonstrate, and are expected to be confirmed and strengthened over the next few years.

Two-Phase Model for Demand-Responsive Transit Considering the Cancellation Behavior of Boundedly Rational Passengers

The resurgence of demand-responsive transit (DRT) has been pushed toward a more sharable and user-friendly mobility service to reshape the urban mobility ecosystem. Nevertheless, to fulfill the swiftly growing diversified and personalized demand, how to depict the cancellation behavior of passengers remains an often overlooked but extremely significant challenge. This paper attempts to discuss the cancellation behavior in the two-phase optimization process. With regard to the unconfirmed reservation cancellation, bounded rationality is incorporated to portray the decision-making process of passengers. Bernoulli random variable is utilized to discuss the stochastic possibility of order cancellation. Simultaneously, a two-phase biobjective model considering the cancellation behavior is constructed to balance the inherent trade-off between passengers and operators. In addition, the algorithm based on multidirectional local search is constructed to achieve the Pareto front for the proposed model. Small-scale experiments in the Nguyen-Dupuis network are illustrated to validate the effectiveness of the algorithm. Subsequently, the Beijing case is further exemplified to evaluate the performance of DRT service considering the cancellation behavior. A reasonable penalty mechanism contributes to ensuring operating profit while substantially diminishing the penalty cost. Therefore, this paper facilitates not only capturing the cancellation behavior of passengers but also developing a more flexible and cost-effective DRT service.

Towards a more flexible demand responsive transit service with compensation mechanism considering boundedly rational passengers

AbstractDemand responsive transit (DRT) with app‐based reservation platforms is experiencing a renaissance to bring the tremendous potential for mobility in the urban universe. Nevertheless, how to attract and retain passengers for long‐term use has become one of the most significant problems. The decision‐making psychology of passengers is often overlooked but incredibly critical in the practical applicability of DRT services. This paper proposes a more flexible DRT service with soft time windows considering boundedly rational passengers. A compensation mechanism is developed to alleviate the dissatisfaction of passengers while considerably promoting the system efficiency. A two‐stage model is designed to incorporate bounded rationality into the optimization process of mixed demand, including the static phase for reservation passengers and the dynamic phase for real‐time passengers. To enhance the computational efficiency, a hybrid heuristic algorithm combining spatiotemporal clustering and non‐dominated sorting genetic algorithm (NSGA)‐II is constructed to obtain the Pareto solutions set. An illustrative example of the Nguyen–Dupuis network is presented to demonstrate the validity of the algorithm. Subsequently, a large‐scale case study in Beijing evaluated the applicability of DRT in the real‐world network. The results reveal that dynamic DRT with compensation mechanism can substantially improve the system performance while ensuring the service quality. The response rate of passengers has been dramatically promoted to 80%. The operating profit has been enormously improved by up to 73%. Therefore, this study is radically conducive to understanding the passenger's decision‐making psychology while constructing a more cost‐efficient flexible strategy for the service provider.

Planning Flexible Bus Service as an Alternative to Suspended Bicycle-Sharing Service: A Data-Driven Approach

Many free-floating bicycle-sharing (FFBS) operators in cold region cities will put bicycles in warehouses and suspend services in winter due to factors such as safety and maintenance costs, resulting in the corresponding travel demand no longer be met. Considering the short-distance and green travel characteristics of FFBS, the flexible bus, as a sustainable demand-responsive transit service, is a suitable alternative transportation mode. To operate such a flexible bus system, service area and operation time planning is a key stage, however, the planning methods in relevant studies are not suitable for this research scenario. In view of the above, this paper proposed a data-driven method to determine the service area and operation time of flexible buses based on FFBS data. Firstly, an FFBS trip path reconstruction algorithm consists of fine-grained road network modelling and trajectory matching is proposed. Then, in the defined time slice, incorporating the idea of ride-sharing routes generation, according to the density-based clustering principle and considering topology between trajectories, a path clustering algorithm PATHSCAN is developed to generate the one-day path clusters. After that, a frequent pattern mining algorithm is applied to the multiday path clusters, and frequent pattern results with spatio-temporal correlation will be merged into the final service area. The generated planning results will cover ride-matching trips and high-frequency riding paths. Detailed application analysis and verification are carried out by using the real data from Tianjin, China. Through the evaluation and verification under the relatively limited experimental data set, the proposed data-driven method shows ideal planning results. Flexible bus service can supplement the green short-distance travel mode after the suspension of FFBS and can avoid FFBS travel demands switched to unsustainable transportation modes to a large extent. This study will contribute to urban sustainable transportation development and improving greenness.

Transit services and user satisfaction: Application of latent class cluster analysis

Past studies have focused mainly focused on studying different aspects of traditional flexible and fixed route transit services, but little attention has been paid towards ridesharing services available to university community. To bridge this gap, this study is aimed at classifying university community based on their satisfaction levels towards demand responsive transit services available to them using a Latent Class Cluster Analysis (LCCA) approach. We employ LCCA models to find out the clusters of users based on their perceptions towards several service performance attributes of three ridesharing services that serve in the University of Texas at Arlington community. Results show that younger, women and low-income populations are more likely to be satisfied as compared to older, men and high-income populations. We also find that white and domestic students are more likely to be satisfied than Asian and international students. Respondents from households without a vehicle were also more likely to be satisfied than users with more than one vehicle in the household. Findings from this study could be used to understand how different groups of users perceive the service performances of these ridesharing services and this could help transportation planners and services providers to improve the efficiency of their services.

Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing.

Electric demand-responsive transit routing with opportunity charging strategy

This study proposes a novel model for the design of electric demand-responsive transit (e-DRT) services, in which the vehicle service plan and opportunity charging plan can be optimized simultaneously. In particular, all recharging plans are well coordinated with flexible transit service schedules. A customized variable neighborhood search is developed to solve the proposed model efficiently. Real-world cases are presented to evaluate the system performance and justify the capability of the proposed method. Comparisons between Cplex and the heuristic suggest that the proposed algorithm can considerably improve computational efficiency. A comparative analysis shows the proposed model takes 11% less total cost than the alternative model with full charging strategy. Sensitivity analyses show the impacts of charging power, bus capacity, availability of fast chargers, and initial state-of-charge on the e-DRT’s performance.

장애인 교통수단 효율성에 대한 핵심 결정요인 분석

The demand for demand-responsive transit (DRT) services has continuously increased for vulnerable transport users such as the elderly and disabled. DRT operators must maximize efficiency because their fares are regulated by law despite their high operating costs. Although a comprehensive evaluation of the efficiency of DRT services is imperative, little work has been done in this area. The main objective of this research is to identify the variables that significantly affect the operational efficiency of DRT service providers and propose ways to improve the competitiveness of DRT services. This study presents a new approach to analyzing the efficiency of DRT operators and investigating the determinants of their efficiency. The results indicate the significant impacts of minimum reservation time, maximum reservation time, and operational experience on efficiency. Unlike previous studies, however, ownership type does not significantly affect efficiency. This study presents guidance for policy makers and managers who want to develop transparent management strategies.

Joint Optimization of Zone Area and Headway for Demand Responsive Transit Service under Heterogeneous Environment

This paper presents a mathematical model to optimize zonal demand responsive transit (DRT) considering heterogeneous environment (i.e., community boundary, land use, demand distribution, line-haul travel time, etc.) under the advent of Mobility-as-a-Service (MaaS). Since most previous models over-simplified conditions of the DRT service area, we propose a new modeling approach to formulate the operator and user costs. Passengers with varied expectations of vehicle arrival time at a drop-off location are considered. The average cost is minimized through optimizing service zone areas and associated headways subject to practical constraints (i.e., policy headway and vehicle capacity). A real-world region in the City of Calgary, Canada, is applied to demonstrate the applicability of the model. The impact of real-time vehicle arrival information to the optimal solution is assessed. The relationship between system parameters (i.e., line-haul travel time, demand density, vehicle capacity, and passenger composition, etc.) and the optimized solutions (i.e., zone area, headway, and costs) is explored through the sensitivity analysis.

Exploring the subscribing behavior of customized bus passengers: Active users versus inactive users

Understanding the mechanism of continuous subscribing behavior is vital to the operation and even survival of customized bus (CB) systems. Capturing the differences in subscribing behavior of active and inactive users and developing targeted and differentiated marketing strategies accordingly will help to improve the user retention rate. The results of this study revealed differences in the subscribing behavior between active and inactive users and the influence of a wide range of factors on the subscription behavior between the two groups, such as area-related and individual characteristics. Travel distance presents an inverted “U” shape effect on the propensity to use CB with the vertex at around 16 km for active users and 28 km for inactive users, which is a further finding of the monotonic positive effect indicated by the existing studies. For every RMB 10 cheaper CB trip, the likelihood that an active user holds a higher “propensity” to use CB services will increase by 8%. The cost advantage will attract novelty-seeking travelers to try CB services occasionally, but cannot convert them into high-frequency users. The passengers who started to subscribe to the CB service in winter are more likely to travel by CB frequently no matter in active and inactive user groups. The findings of this empirical study lay a theoretical foundation for ridership retention and marketing orientation of the customized and demand-responsive transit services.

Investigating the Spatiotemporal Imbalance of Accessibility to Demand Responsive Transit (DRT) Service for People with Disabilities: Explanatory Case Study in South Korea

This study analyzed the operation-related historical data of the call taxi service for disabled people in Seoul, South Korea. The study investigated how unevenly distributed the accessibility of disabled people to transportation is in terms of time and space. In addition, the reasons that cause imbalanced accessibility were investigated in areas with good and poor accessibility. Accessibility was defined as how quickly call taxi services for the disabled are available at specific times and locations. For the analysis, the log data for tracking the status of taxis in time and space were processed to calculate their availability, an index that reflects the dwelling time and the number of taxis available at a specific time and in a specific area. This index was divided into time and space and used as a surrogate measure to assess accessibility. The results showed that there were spatial and temporal accessibility imbalances in demand responsive transit (DRT) service. The insufficient supply during the night resulting from the current DRT operating schedule has reduced the accessibility of call taxis for the disabled, and the concentration of drivers’ breaks also affected the accessibility of service during the daytime. This suggests the need for (1) an increase in supply and (2) evenly distributed breaks for the drivers. In terms of space, the outer areas of Seoul generally were found to be more accessible than the central areas. In addition, areas near depots that serve as hubs and resting places for taxi drivers, areas with excellent medical infrastructures for people with disabilities, and areas with good traffic environments tended to have good accessibility; this suggests the need to reallocate garages and improve the traffic environments to improve accessibility.

Novel model for integrated demand-responsive transit service considering rail transit schedule.

This research aims to develop an optimization model for optimizing demand-responsive transit (DRT) services. These services can not only direct passengers to reach their nearest bus stops but also transport them to connecting stops on major transit systems at selected bus stops. The proposed methodology is characterized by service time windows and selected metro schedules when passengers place a personalized travel order. In addition, synchronous transfers between shuttles and feeder buses were fully considered regarding transit problems. Aiming at optimizing the total travel time of passengers, a mixed-integer linear programming model was established, which includes vehicle ride time from pickup locations to drop-off locations and passenger wait time during transfer travels. Since this model is commonly known as an NP-hard problem, a new two-stage heuristic using the ant colony algorithm (ACO) was developed in this study to efficiently achieve the meta-optimal solution of the model within a reasonable time. Furthermore, a case study in Chongqing, China, shows that compared with conventional models, the developed model was more efficient formaking passenger, route and operation plans, and it could reduce the total travel time of passengers.

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.

Heuristic approach for the multiobjective optimization of the customized bus scheduling problem

Customized bus is a typical demand-responsive transit (DRT) service that allows passengers to customize their bus route on demand and be picked up and dropped off at their desired location. A set of ride requests sent at various times by geographically dispersed passengers are operated in real time by the controller and then assigned to the shared bus with the assurance of maintaining the minimum vehicle and driver costs, which introduces a new challenge to scheduling. In this paper, a mixed integer linear programming (MILP) model is presented to solve the customized bus scheduling problem. To quickly generate satisfactory scheduling solutions in the case of single- and multi-depots, a heuristic multi-objective optimization algorithm based on preemptive scheduling is proposed, which can converge to the optimal solution among a set of vehicle blocks (consecutive trips by one bus). A real-world customized bus scheduling problem from the public transport company of Chengdu, China that contains 2,000-trip instances operated on a day is used to demonstrate that the proposed approach reduces total operating costs by 30.51% and 30.71%, compared with the simulated annealing algorithm and genetic algorithm, respectively.