Demand Responsive Transport Research Articles

Modeling demand responsive service for the elderly healthcare activities considering temporal and spatial variations

ABSTRACT This paper develops a cost-effective demand-responsive transportation (DRT) model aimed at enhancing the accessibility and convenience of clinical visits for the elderly by optimizing two NP-hard problems. First, a set of pick-up stops is optimized based on potential stop locations, spatial and temporal demand, and acceptable walking distance. Next, vehicle routing and scheduling are optimized, considering vehicle capacity, users’ desired arrival times, and travel companions, to minimize total costs. An elite genetic algorithm (EGA) is developed to effectively search for the optimal solution. In the case study, demands are generated from a survey on elderly clinical visits and population census data in Xi’an. The proposed EGA is applied to different clustered demands classified by time interval to identify optimal solutions. Sensitivity analysis is conducted to explore the relationships between model parameters and decision variables for the studied DRT system.

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.

Optimizing Fleet Size in Point-to-Point Shared Demand Responsive Transportation Service: A Network Decomposition Approach

With increasing urbanization and the demand for efficient, flexible transportation solutions, demand-responsive transportation services (DTRS) has emerged as a viable alternative to traditional public transit. However, determining the optimal fleet size to balance the investment and operational revenue remains a significant challenge for service providers. In this article, we address the optimization of fleet size in point-to-point shared demand DRTS, which widely operates within many cities. To capture the uncertain passenger demands in the future when planning the fleet size currently, we model this problem with a framework of two-stage stochastic programming with recourse. Fleet sizing decisions are made in the first stage before the uncertain demands are revealed. After the uncertainty is revealed, the second stage involves making additional decisions to maximize operational revenue. The objective is to optimize the total revenue of the first-stage decisions and the expected revenue of the recourse actions. To solve this practical problem, we resort to the Model Predictive Control method (MPC) and propose a network decomposition approach that first converts the transportation network to a nodal tree structure and then develops a Nodal Tree Recourse with Dependent Arc Capacities (NTRDAC) algorithm to obtain the exact value of the expected recourse functions. In the experiments, NTRDAC is able to produce results within seconds for transportation networks with over 30 nodes. In contrast, a commercial solver is only capable of solving networks with up to five nodes. The stability tests show that NTRDAC remains robust as the problem size varies. Lastly, the value of the stochastic solution (VSS) was evaluated, and the results indicate that it consistently outperforms the expected value solutions. Numerical experiments show that the performance of the NTRDAC algorithm is quite encouraging and fit for large-scale practical problems.

How successful is my DRT system? A review of different parameters to consider when developing flexible public transport systems

The digitisation of transport services has led to the implementation of so-called demand-responsive transport (DRT) systems with varying setups and goals around the globe. Numerous approaches have been made to account for differences in the local circumstances and regulations to achieve the most successful outcome for those services in both, rural and urban areas. The reasons for their widespread popularity are the envisaged benefits of economically reasonable operations combined with ecologically and socially beneficial traffic. Yet, while some DRT systems persist over an extended period, many fail to become a permanent passenger transportation option, especially in rural areas.This paper shall explore parameters suited to measure the success of a DRT system and structure their inter-dependencies. Firstly, the paper presents types of flexibility and their different gradations in order to classify varying systems. Secondly, suitable and already used parameters to measure figures of DRT systems like travelled distances and vehicle occupations are examined and with their respective application cases lucidly compared. Thirdly, the paper proposes a four-step evaluation framework and discusses existing and new approaches to comparing the success of different flexible transport systems and future possibilities.

Factors Influencing the Efficiency of Demand-Responsive Transport Services in Rural Areas: A GIS-Based Method for Optimising and Evaluating Potential Services

Factors Influencing the Efficiency of Demand-Responsive Transport Services in Rural Areas: A GIS-Based Method for Optimising and Evaluating Potential Services

Exploring Economic and Ethical Challenges of Implementing Demand-Responsive Transport Systems (DRT) in Italy

In recent years, a change in demand trends has influenced the development of public passenger transport, causing a number of critical issues and consequences for the environment, the management policies of these services and, especially, the users. Various passenger transport services have evolved to reduce the use of private vehicles, including DRT systems that consider technological developments like Mobility ad a Service (MaaS). This article focuses on a European literature review on user-oriented research development. The overall objective is to: i) highlight, among all the critical aspects of service development, the factors influencing the economic and ethical aspects; ii) finally identify a discrepancy between the perception of DRT and the empirical research design. The study concludes by providing some potential suggestions for improving DRT service deployment and reducing some of the factors influencing users' modal choices. Finally, the paper proposes implications for policy makers and operators.

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

How social innovations emerge in a rigid regulatory context: the case of demand responsive transport in Switzerland

ABSTRACT Demand responsive transport (DRT) solutions may improve rural transport systems. These solutions often emerge as social innovations in rural areas. In Switzerland, their planning process is embedded in a rigid regulatory framework of the public transport system. The objective of this paper is to understand in which way the regulatory framework of the public transport system influences planning processes of a DRT solution in a rural setting. The methodological approach of social innovation biographies helps to examine the necessary planning steps to start mybuxi, a DRT in two Swiss rural municipalities. Our findings show that regulatory constraints dominate the final planning stage of the social innovation. Protections of current public transport regulations hinder federal offices from subsidizing the DRT, having direct implications on the financial sustainability of the system. The findings of this paper help actors to better address regulatory questions in the early stage of the respective innovation process.

A scalable combinatorial benders decomposition for the Stochastic Dial-a-Ride Problem

Nowadays, Demand-Responsive Transport companies can help the elderly or disabled citizens in their everyday journeys as well as provide an important link between big cities and their outer suburbs. In practice, many of those trips are scheduled in advance but many others are asked on the fly (online) by the users while the drivers are already on their routes. To answer those online requests, dispatching centers often rely on fast heuristics based on some myopic objectives. However, this deteriorates the flexibility of the dispatch over time. This paper shows the potential of a thorough optimization of the planning of the vehicles at the start of the day under the hypothesis that the transport company is able to forecast the requests that will arrive during the service. Our objective is to design an initial set of routes for the vehicles that maximizes the expected acceptance rate of future requests, since the ones scheduled in-advance are mandatory. In this prospect, we propose a Combinatorial Benders Decomposition framework to solve the so-called Stochastic Dial-a-Ride Problem. A key ingredient for the success of this solver is the use of a clustering-based initialization of the Master Problem. The computational results show the effectiveness of this approach when applied to instances extracted from the literature as well as from actual services provided by Padam Mobility, an international company in Shared Mobility Systems. The proposed method provides a substantial performance gain though traditional heuristics are kept to manage online insertions afterward.

Corrigendum to “Integrated demand responsive transport in low-demand areas: A case study of Canberra, Australia” [Transp. Res. Part D: Transport Environ. 127 (2024) 104036

Corrigendum to “Integrated demand responsive transport in low-demand areas: A case study of Canberra, Australia” [Transp. Res. Part D: Transport Environ. 127 (2024) 104036

A multi-task deep learning framework for forecasting sparse demand of demand responsive transit

Demand Responsive Transport (DRT) is a flexible transportation service that adjusts its routes and schedules in real time based on user requests. Real-time demand forecasting has become a timely issue since the resolution of transportation planning has increased to provide flexible services to users. Although many previous studies have been conducted to design the demand forecasting model using the mobility service historical data, there are more considerations when predicting real-time demand for on-demand mobility services. Therefore, this study aims to develop a demand forecasting model for DRT services addressing two issues. First, repeated calls for service and abandoned requests are converted into the unit of demand to capture served and potential demand. Second, given the sparsity of DRT historical data, a multi-task deep learning model is proposed to predict both the probability and volume of demand simultaneously to mitigate the data sparsity problem. A zero-inflated loss function was introduced to consider the skewed demand distribution. To attain our goals, the historical dataset of the I-MOD service trial in Yeongjongdo, Inchon, Korea was used. The performance of the proposed model is compared against several baseline models, demonstrating its superiority in forecasting demand for mobility services. The results show that a multi-task structure can alleviate the data sparsity problem by excluding where demand is not generated from the forecasting problem. The proposed models can provide insights into real-time demand forecasting with sparse demand.

Exploring Willingness to Pay across Different Passenger Traits

In recent years, the transportation landscape has seen a notable shift towards shared mobility, driven by its advantages over traditional systems fraught with inefficiencies. This shift has ushered in demand-responsive transport (DRT) systems, enabled by mobile technology. DRT offers a promising solution for the sustainability of public transport, also for rural Slovakia; however, higher individual journey costs and the necessity for online booking create adoption barriers for this new form. This paper conducts a thorough willingness-to-pay analysis in seven rural Slovak settlements, with the goal of understanding variations and comparing the findings with the existing research. Location, household size, age, car and train usage, and previous experiences with bus transport significantly impact the willingness to pay. Consequently, tailored DRT strategies are necessary for different target groups. To maximize the efficiency of this service, integrating DRT into the regional or national public transportation network is recommended. Insights from this study hold significant implications for policymakers tasked with transforming regional transportation, particularly by integrating DRT into fixed-route bus systems.

Sustainability Trade-Offs in Climate Change Geographies in England

The evidence that climate change is the result of human actions is becoming increasingly stronger, as is the need to take action to limit the worst effects of climate change on the planet. However, politicians continue to equivocate and fail to address the trade-offs which are needed to deliver effective action. In this paper, we report on the potential of bottom-up approaches to transport planning to address the trade-offs between the need to reduce car-based travel and the social consequences of poor mobility options in rural areas. Using the theories of Sustainable Communities and Communities of Practice, we analyse the implementation of the Robin demand-responsive transport service in the West of England, presenting new data relating to the effectiveness of this service in providing low-carbon transport alternatives to rural residents. We find that the Robin is indeed effective, and that it has worked better in one location, where engagement with potential new users of the service has been prioritised. We conclude that such bottom-up transport options can be transformative, subject to the support of key stakeholders and integration with top-down systems of governance.

Configurations and features of demand responsive transports

The concept of Demand Responsive Transport (DRT) has been around for more than 40 years and is a promising mobility alternative when traditional public transport proves inadequate in terms of its effectiveness or efficiency, as is the case of low-density areas. DRT systems have a wide range of operational configurations, being highly adaptable to different contexts and environments. Therefore, the design of a DRT mobility solution can become a quite complex and challenging problem. To assist in the design of DRTs, this paper aims to present a comprehensive classification of DRT features and to identify some common design choices in different operational scenarios. The proposed classification is based on a review of reports from available literature and previous European DRT projects. In addition, an analysis of the most usual configurations for different purposes and scenarios is presented. In this research, the operational, demand, and administrative characteristics of DRTs are addressed. Demand aspects encompass features that directly influence trip demand, such as service areas, target passengers, and hours of operation. Operational features include characteristics that will affect daily operations as the type of stops, frequency of the operation, booking methodology, vehicle route, pick-up and drop-off locations, and the vehicle type used. Administrative characteristics address the relationship between consumers and the system, such as the purpose of the system, fares, visual identification of stops, and booking methods. Regarding the usual design choices, our survey shows that rural DRTs are primarily oriented to serve populations in need in low-density areas and to complement existing PT gaps, while urban DRTs are mainly viewed as a mobility alternative to fill existing PT gaps. Defining design patterns for peri-urban and multi-area DRTs presents challenges due to their transitional nature, thus combining attributes of both rural and urban systems.

Data‐Driven Approach to Evaluate the Level of Service (LOS) of Demand‐Responsive Transport for the Disabled (DRTD) with an ANFIS Algorithm

Even though the Demand‐Responsive Transport (DRT) service plays an important role to provide more convenience to the disabled people, they still have a challenge to be solved in terms of waiting time. Especially, the biggest problem is that waiting times for using the DRT service are variant across space and time because users cannot predict when they can get the service and/or how long they have to wait for service. This study developed a model for predicting the waiting time of Demand‐Responsive Transport for Disabled (DRTD) with irregular spatiotemporal characteristics in real time. The primary purpose of the model developed was to monitor the level of service (LOS) to improve the satisfaction of users who use mobility services for the disabled and efficiently manage service providers. The model was estimated using an Adaptive Neuro‐Fuzzy Inference System (ANFIS), which is known to have an excellent predictive performance by combining the advantages of both artificial neural networks and fuzzy inference systems. Four variables, including the number of calls (or requests), the number of vacant vehicles, Medical Infrastructure Concentration Index (MICI), and Disabled Population Concentration Index (DPCI), were used as input variables for the ANFIS‐based model. Despite using cross‐sectional data, the accuracy of the predicted model was found to be excellent (90%) and showed good and even prediction performance without bias by LOS categories The proposed method is expected to become a monitoring tool to manage mobility services and improve user convenience by notifying users how long they have to wait to use mobility services in real time.

Investigating the impact of stop network designs on DRT operation

Modern on-demand mobility concepts have the potential to drive the mobility transition. Increasing attention is being paid to DRT (Demand Responsive Transportation) services that enable the bundling of trips with the help of dynamic routing algorithms. Many providers use a stop-based operating concept for this purpose. The transport between so-called virtual stops promises a higher fraction of shared trips and shorter vehicle detours. The aim of this paper is to investigate multiple stop network designs and their impacts on the efficiency of the operation. For this purpose, three stop network generation approaches were derived from literature and applied to the simulation environment MATSim. A scenario in the city center of Berlin is simulated, where all inner car trips are replaced by DRT. Comparing the stop network design against a door-to-door service, the results suggest a trade-off between a reduction of vehicle kilometers (up to 18%) and increased walking distances (up to 250 meters on average).

Efficient operation of demand-responsive transport (DRT) systems:Active requests rejection

Demand-responsive transport (DRT) is getting more attention in the public sectors. In DRT systems operated by public sectors, providing a good and equal service to the public is more important than maximizing the revenue. This brings additional challenges to the operation of DRT systems. In this study, we explore the idea of actively rejecting requests in DRT systems based on the alternative travel options, such as conventional public transport. By rejecting requests that have good alternatives, we can focus on the passengers without suitable alternative travel options. This can reduce the fleet size of DRT systems while maintaining a good and fair service. To explore the impact of this idea, we develop and implement an active request rejection mechanism in an agent-based transport simulation framework and carry out simulations on a real-world scenario. Results have shown that a fleet size reduction of around 20% can be achieved, without compromising the overall mobility level in terms of travel time, if the active request rejection mechanism with dynamic threshold is used. Furthermore, results have also shown that when a DRT system is overloaded, it may break down, and the service quality will suffer a significant drop. The active request rejection mechanism can prevent this from happening in some cases.

МЕТОД ОЦЕНКИ ЦЕЛЕСООБРАЗНОСТИ ВНЕДРЕНИЯ ТРАНСПОРТА ПО ЗАПРОСУ В ГОРОДСКИХ И ПРИГОРОДНЫХ ЗОНАХ

. In the modern world, environmental issues and reducing traffic congestion are becoming prior, so the new transport systems organization approaches development and implementation is an integral part of the city and regional development. Demand Responsive Transport, also known as «on-demand transit», is a system where passengers can order vehicles to move around a city or area. Now, such systems are successfully used in major cities around the world and demonstrate their effectiveness in reducing traffic congestion, reducing emissions of harmful substances and saving passengers’ time. The implementing feasibility assessing method allows determining how effective and appropriate it will be to implement an on-demand transit for a specific suburban and urban area.

En-route transfer-based dynamic ridesharing

In the last few decades, the continuous growth of travel demand increased urban traffic congestion. The situation demands an efficient way of handling resources like roads and vehicles. Parallelly, the advancement of technology proposed more innovative forms of shared mobility, such as flexible pods. These autonomous, modular vehicles possess the unique capability to autonomously merge and split during their journeys, facilitating en-route transfer of passengers. Formulating a precise mathematical model that comprehensively captures the intricacies of such a complex system is quite challenging. This paper introduces an alternative approach rooted in Reinforcement Learning that circumvents the need for a predetermined mathematical model. Instead, it enables the system to learn its dynamics through interactions with the environment. The primary goal of this research is to acquire an optimized passenger transfer policy. The experimental findings indicate that the transfer-based pod service outperforms private taxi services and Demand Responsive Transport services, boosting vehicle utility by 18% and 8%, respectively. Remarkably, the pod service achieves these gains with only one-third of the vehicles required for private taxis and 13% fewer vehicles than the Demand Responsive Transport service. However, it's important to note that the transfer-based pod service does result in a modest increase in passenger travel time, ranging from 2% to 13%. Therefore, the transfer-based pod service offers a promising opportunity to alleviate traffic congestion while accepting a compromise on passenger travel times.

Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method

Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method