Demand Responsive Transit Systems Research Articles

Optimizing service design for the intercity demand responsive transit system: Model, algorithm, and comparative analysis

Intercity Demand Responsive Transit (IDRT) systems, providing on-demand “door-to-door” services for intercity travelers, have been recently implemented in a few cities and gained popularity. To devise a theoretical tool for designing efficient IDRT systems, this study proposes a continuous approximation (CA) model to describe the operation metrics of the IDRT. In a two-city system, an optimization model is proposed to study the zoning strategies in the main city, the service area in the satellite city, and headways. The objective is to minimize the average system cost, including the costs of both the operators and the passengers. A two-stage partition algorithm is proposed to generate a near-optimal solution by combining the greedy algorithm and sensitivity-based coordination optimization method. Subsequently, we develop a direct solution algorithm to establish a lower bound for the optimal solution, facilitating a comparison with the near-optimal solution. Throughout all sensitivity analyses, the gaps between the near-optimal solution and the lower-bound solution remain below 3 %. The comparison results demonstrate that the computation time and solution quality of the two-stage partition algorithm are considered acceptable. Simulation is performed to test the validity of the CA model, and the errors in the average system costs are less than 1.16 %. Sensitivity analyses are carried out to evaluate the impacts of several key parameters (e.g., demand densities and line-haul distances) on the optimization results. By comparison with Intercity Fixed Route Transit (IFRT), it can be seen that the choice of IDRT depends on passengers' preference for door-to-door transportation. This study may serve as a valuable reference for the practical implementation of IDRT.

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.

Robust Service and Charging Plan for Dynamic Electric Demand-Responsive Transit Systems

Robust Service and Charging Plan for Dynamic Electric Demand-Responsive Transit Systems

Applying modified-data mining techniques to assess public transportation vulnerable urban and suburban city areas

To guarantee the right to move for residents in areas where public transportation is insufficient, research is needed to identify vulnerable areas and prepare measures. This paper defines the vulnerable regions of public transportation within various city types in Korea. In order to identify appropriate areas to apply the Demand Responsive Transit (DRT), the regions with vulnerability were compared with a specific city (Yangsan-si) which already the DRT system was successfully adopted. To collect monthly bus data, web-data crawling method was performed and processed with coordinating program by matching GPS coordinate. The public transportation demand was predicted for each grid cell size (100 m, 250 m, and 500 m) by different methodologies. Various data mining models based on regression were analyzed to predict bus demand of vulnerable areas. Among models, a modified model was suggested to combine Automated machine learning models for high prediction performance. The modified model outperformed other methods as 0.685 and prediction performance was appropriate at 100 m rectangle grid. Regional characters of DRT bus allocation areas were extracted by K-means clustering method and differentiate urban and suburban types. The findings of this study provide valuable insights into conditions that DRT bus stop can be installed. The urban bus stop areas located in metropolitan cities and the suburban bus stop allocation areas located in countryside. The study results can be used as policy data for the successful introduction to prevent social exclusion and improve resident welfare in the future.

Understanding Autonomous Shuttle Adoption Intention: Predictive Power of Pre-Trial Perceptions and Attitudes

The capability of 'demand-responsive transport', particularly in autonomous shared form, to better facilitate road-based mobility is considered a significant advantage because improved mobility leads to enhanced quality of life and wellbeing. A central point in implementing a demand-responsive transit system in a new area is adapting the operational concept to the respective structural and socioeconomic conditions. This requires an extensive analysis of the users' needs. There is presently limited understanding of public perceptions and attitudes toward the adoption of autonomous demand-responsive transport. To address this gap, a theory-based conceptual framework is proposed to provide detailed empirical insights into the public's adoption intention of 'autonomous shuttle buses' as a form of autonomous demand-responsive transport. South East Queensland, Australia, was selected as the testbed. In this case study, relationships between perceptions, attitudes, and usage intention were examined by employing a partial least squares structural equation modeling method. The results support the basic technology acceptance model casual relationships that correspond with previous studies. Although the direct effects of perceived relative advantages and perceived service quality on usage intention are not significant, they could still affect usage intention indirectly through the attitude factor. Conversely, perceived risks are shown to have no association with perceived usefulness but can negatively impact travelers' attitudes and usage intention toward autonomous shuttle buses. The research findings provide implications to assist policymakers, transport planners, and engineers in their policy decisions and system plans as well as achieving higher public acknowledgment and wider uptake of autonomous demand-responsive transport technology solutions.

Built environment as a precondition for demand-responsive transit (DRT) system survival: Evidence from an empirical study

Media coverage of the convenience, flexibility and personalized travel services of demand-responsive transit (DRT) systems masks the fact that these systems have a high failure rate worldwide. The failed cases suggest that DRT does not necessarily survive in all types of human settlements; however, existing studies have failed to give quantitative assessments of how and the degree to which the built environment impacts DRT use. This paper addresses this problem through an empirical study of a successful DRT system in Dalian, China. The results demonstrate a significant impact of the built environment on DRT use. A larger residential population, lower employment density, more residential land uses, less administrative land uses, poor connectivity to road networks and parking supply, better accessibility to different facilities, and long distances from urban business centers encourage DRT use and are therefore conducive to the development of customized bus (CB) systems (routes). The effects of built environment characteristics on DRT use were found to be largely different from those in regular transit systems, indicating that tapping into niche markets that are poorly served by regular transit services is a potential marketing direction for DRT systems. The findings of this study can provide theoretical guidance for DRT operators to identify appropriate market niches and thereby reduce the risk of failure.

A novel model and algorithm for designing an eco-oriented demand responsive transit (DRT) system

Since the flexibility and adaptability of demand responsive transit (DRT) give more potentials and spaces of embracing eco-routing strategy, this study develops a novel framework of embedding eco-routing technology into DRT service, in which module of vehicle dynamics and module of fuel consumption/emission are specifically integrated in response to varied traffic conditions and road types when designing of DRT system. To this end, a mix-integer model and the customized DP-based heuristic are proposed to determine eco-oriented DRT routes and schedules. A series of numerical cases based on realistic road networks are designed to unfold the comparison of the performance of eco-oriented DRT against the traditional one. The results demonstrate that introduction of eco-routing is able to significantly reduce the energy consumption and emissions by up to 37% in companying with a slight increase of total travel time and routing distance. The well-developed models and algorithm make the study is ready for real-world applications.

A simulation-optimization model for analyzing a demand responsive transit system for last-mile transportation: A case study in São Paulo, Brazil

Ride-hailing services, in particular Demand Responsive Transit (DRT) systems, have emerged in many cities worldwide as a potential solution to bridge the public transport supply gap. In such services, passengers whose itineraries and times are coincident to a certain degree can share vehicles that follow flexible routes to accommodate distinct boarding and alighting locations with minor disturbances to their convenience. In this context, this paper aims to evaluate the potential of a DRT as an alternative for a feeder to a trunk transport system, such as BRT (Bus Rapid Transit), or a metro or rail system as well. This is accomplished by a simulation–optimization model that allows determining, in very short time, the optimized way to serve each new transport request that arises dynamically over time. The model was applied to a first/last mile transportation system in an area adjacent to a subway station in the city of São Paulo, Brazil. We analyze and measure the impacts of different operational characteristics of supply and demand, measured in terms of service level to riders, number of vehicles, average occupation and total kilometer traveled per vehicle. The results evidence the potential of these DRT services as an alternative to complement the existing bus lines, as well as to attract users of individual transport, as they can offer adequate service level at a competitive cost.

The bus-type taxi: a better demonstration of ride-sharing than Uber Taiwan

ABSTRACT This study addresses a capacitated vehicle routing problem with time windows and simultaneous pickup and delivery (VRPTWPD) for scheduling bus-type taxis formerly served by regular buses. The effects of varying the maximum number of passenger seats on service quality and costs are investigated. Taiwan's government has subsidized public transport in order to ensure consistency between service shifts and demands, improve the efficiency of public transport operations, and maintain an ongoing supply of services while reducing the number of scooters and/or private cars on roads to minimize traffic congestion. Ultimately, the intent is to reduce government subsidies further and maintain service quality at reasonable levels. Transit planners have also introduced the Demand Responsive Transit System (DRTS) to connect routes. Particularity in rural areas, local transit planners have replaced buses with taxis to form a bus-type taxi service that increases efficiency. As a consequence, the transit service in low-density or rural areas has not been abandoned. The study acknowledges that optimal routes for successful bus-type taxis services vary by area. Routing and scheduling results will assist route planners' efforts to provide sufficient transit services in rural areas. The study results also suggest that further reductions in government subsidies are possible.

Path-Based Dynamic Vehicle Dispatch Strategy for Demand Responsive Transit Systems

Demand responsive transit (DRT) has the potential to provide passengers with higher accessibility and lower travel time as compared with conventional transit, and at the same time make more efficient use of vehicle capacity than traditional taxi. In many current systems, vehicles are assigned to passengers along travel paths that are chosen myopically. When information on future demand distribution is available, it would be more beneficial to dispatch transit vehicles strategically to areas with a higher probability of generating passengers. This paper proposes a mathematical model for a dynamic DRT vehicle dispatch problem. It determines in real time how the operating vehicles shall be used to serve arriving passenger demand, and which paths the vehicles should choose to achieve a balance between operator and passenger costs. The model is solved by an approximate dynamic programming (ADP) based solution approach. Case studies, including a hypothetical numerical example and a real-world case in Qingdao, China, have been conducted to demonstrate the applicability of the proposed modeling framework. Results show that the proposed ADP solution can significantly improve the overall system performance as compared with myopic benchmarks.

Evaluating demand responsive transit services using a density-based trip rate metric

Demand responsive transit (DRT) is attracting increased attention as a means to provide public transit to low-density populations. This research aims to provide a suite of evaluation metrics with low data requirement and widespread availability, so that operators, funders, regulators, and practitioners can better evaluate the performance of DRT services. Trip numbers can be divided by a number of available variables (period, trip length, population, and density) to create a number of derived metrics. By applying these variables across three different DRT service areas in Logan City, Australia, where other key factors are held constant, one can see how different formulations lead to very different readings of DRT system performance. The results confirm the dilemma of cost efficiency versus equity in service provision in low-density environments. This paper also highlights current data limitations and calls for better data collection to facilitate the development of new evaluation methods for DRT services and a new composite metric that can be used for inter-service comparison.

Design and Implementation of Zone-to-Zone Demand Responsive Transportation Systems

Conventional fixed-structure transit service is unable to satisfy the increasingly personalized passenger demand. Shared mobility companies enable the transformation of conventional transit with advanced technology. This paper proposes a framework to design an application-based demand responsive transit (DRT) system to serve zone-to-zone passenger requests. The proposed DRT service operates between zone pairs to satisfy “door-to-door” and “reservation-based” requests. A two-phase problem is formulated, including (i) conceptual planning phase and (ii) discrete route design phase. In the conceptual planning phase, a continuous approximation model is proposed to optimize operational efficiency by deciding the resource requirements and operational features with given service area and demand density. In the route design phase, a customized bus routing model is established to generate profitable routes, by adopting a combined solution approach with adapted savings and simulated annealing (SA) method. The theoretical framework is applied in a pilot area at Huangdao College Town in Qingdao, China. An implementable design including the layout plan, candidate stops, and operation headways is developed from the conceptual plan model. Groups of sample data sets are used to demonstrate the applicability of the proposed design framework. Results show that the proposed DRT system is promising in relation to providing better service than the previous first-come-first-serve bus pooling service.

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.

Analytical models for comparing operational costs of regular bus and semi-flexible transit services

The provision of public transit services is essential to provide citizens with an improved quality of life. The most popular fixed route bus service (FRBS) is intended to serve a large population along with fixed stops, routes, and schedules. However, in low demand conditions, bus transit operators pay a high cost per passenger to maintain the desired level of service. A demand-responsive transit (DRT) is a commonly discussed transportation solution for serving low passenger demand. Although the conventional DRT system offers a shared-ride door-to-door service to passengers, its implementation is challenging and expensive during peak hours. The present study suggests a form of transit system that combines the rigidity of FRBS and flexibility of DRT to operate in low demand routes. This defined category of transit service is referred to as semi-flexible transit (SFT) system. The proposed SFT service is delivered along the fixed bus route and a limited number of fixed stops based on a flexible schedule to meet passenger requests. The defined SFT system offers reduced operating time and higher vehicle utilization. The study considers an operation of the proposed SFT service for two types of service delivery systems: Contract-Out Taxi Service (COTS), and In-House Paratransit Service (IHPS). A methodology is proposed to develop analytical models describing operational costs of regular FRBS, COTS, and IHPS as a function of demand or annual ridership. Operating cost models are proposed as a decision support tool to enable transit planners to determine the passenger demands along the route at which it is justifiable to “switch” from FRBS to the SFT system to minimize the total cost of operation. Further, the analysis results for FRBS indicate that operating costs are strongly related to vehicle-service hours and for SFT services to average vehicle occupancy rate and deadheading time. Applications of the proposed models and analysis method are demonstrated for a low demand route in the City of Regina.

Demand-responsive transit systems in areas with low transport demand of “smart city”

The development of modern telecommunications allows the concept of a “smart city” to increase the flexibility of public transportation significantly and, thus, improve population mobility. In this regard, public transportation represents a demand-responsive transit (DRT) system. This system occupies an intermediate position between route systems and taxi services and ensures flexibility in providing on-demand transport services. The article describes the main technologies that can be used in the DRT system, including those with fixed routes (Flexible-Route Segments, Request Stops) and without fixed routes (Demand-Responsive Connector, Feeder Service, Zone Route, Point Deviation). Despite the wide international experience in implementing DRT in practice, there are no clear criteria to determine the conditions, under which it is reasonable to introduce a particular technology of flexible service for passengers. The article systematizes the factors that determine the feasibility of transferring a bus route to the DRT system in particular time intervals and also determines a sequence of actions for the introduction of flexible modes of public transport services. The considered technologies to manage public transportation on demand show that it is possible to combine regular traffic and demand-responsive service, using these modes at different times. Such an approach allows reducing the carrier’s costs while maintaining or even improving the quality of public transportation compared with systems implementing only flexible service modes.

An Analytical Model for the Many-to-One Demand Responsive Transit Systems

The demand-responsive transit (DRT) service is an emerging and flexible transit mode to enhance the mobility of the urban transit system by providing personalized services. Passengers can make advanced appointments through smartphone applications. In this paper, an analytical model is proposed for the many-to-one DRT system. The agency and user costs are approximated by closed-form expressions. The agency cost, which is also the operation cost, is approximated by the continuum approximation technique. A nearest-neighbor routing strategy is applied, whereby the vehicle always collects the nearest passenger waiting in the system. The Vickrey queueing theory is adopted as the basis for approximating each component of the user cost, which is composed of the out-of-vehicle and in-vehicle waiting times and schedule deviations, which also depend on the service quality of the DRT system. The results of the numerical experiment show that (1) the agency and user costs are influenced significantly by the demand density, and (2) the DRT operator cannot further decrease the operating cost by solely deploying larger vehicles.

A Threshold Policy for Dispatching Vehicles in Demand-responsive Transit Systems

This paper considers vehicle dispatching for a flexible transit system providing doorstep services from a terminal. The problem is tackled with an easy-to-implement threshold policy, where an available vehicle is dispatched when the number of boarded passengers reaches or exceeds a certain threshold. A simulation-based approach is applied to find the threshold that minimizes the expected system-wide cost. Results show that the optimal threshold is a function of demand, which is commonly stochastic and time-varying. Consequently, the dispatching threshold should be adjusted for different times of the day. In addition, the simulation-based approach is used to simultaneously adjust dispatching threshold and fleet size. The proposed approach is the first work to analyse threshold dispatching policy. It could be used to help improve efficiency of flexible transit systems, and thereby make this sustainable travel mode more economical and appealing to users.

Multi-Objective Model for Dial-a-Ride Problems with Vehicle Speed Considerations

Mobility on demand (MOD) provides improved mobility options to all travelers with the use of on-demand information and real-time data. Several alternatives, such as Demand Responsive Transit Systems (DRTS) services, have been introduced around the world. Early DRTS provide on-demand service for areas of low-density population. Nowadays, DRTS are mostly used to provide door-to-door services, and this specific type of DRTS is called Dial-a-Ride Problems (DARP). In this study, a multi-objective model with three objectives, including travel cost, service quality, and eco-efficiency, is formulated. Travel cost is estimated through vehicle travel time, service quality is measured as customer waiting time, and eco-efficiency is measured through consumed fuel. A speed-level variable is introduced in the DARP model to describe travel time, waiting, and consumed fuel simultaneously. For each objective, a single objective model is constructed and implemented. Then, the weighting method with normalization (WMN) is applied for the multi-objective model to solve three objectives. The proposed model is solved through the Gurobi optimizer. Numerical experiments are conducted based on real geometric data in Kaohsiung City. The results show that the proposed model not only provides compromise solutions, but also improves the total performance in meeting three objectives. Pareto front is analyzed with many different combinations of weights to provide more information about the trade-offs between the three objectives. The results can be applied in practice to design vehicle routes for operators and to design DARP evaluation criteria for official agencies.

Impact assessment of autonomous DRT systems

Abstract The market entrance of shared autonomous vehicles (SAV) may have disruptive effects on current transport systems and may lead to their total transformation. For many small and medium-sized cities, a full replacement of public transport services by these systems seems to be possible. For a transport system operator, such a system requires a bigger fleet of vehicles than before, however, vehicles are less expensive and fewer staff is needed for the actual operation. In this paper, we are using a simulation-based approach to evaluate the service quality and operating cost of a demand responsive transit (DRT) system for the city of Cottbus (100 000 inhabitants), Germany. The simulation model used is based on an existing MATSim model of the region that depicts a typical work day. Results suggest, that the current public transport system may be replaced by a system of 300 to 400 DRT vehicles, depending on their operational mode. Compared to previous, schedule based public transport, passengers do not need to transfer, and their overall travel times may be reduced significantly. Results for the cost comparison are preliminary, but results suggest that an autonomous DRT system is not necessarily more expensive than the current public transport system.

Strategic Planning and Prospects of Rail-Bound Demand Responsive Transit

Fully automated services allow for greater flexibility in operations and lower marginal operational costs. In this study we examined the strategic planning implications of a novel service concept – an automated rail demand responsive transit (DRT) system that offers a direct non-stop service. The objective of this study was to determine the capacity requirements of the envisaged service and discuss its prospects and feasibility. A cost minimization approach for determining the optimal track and station platform capacities for a rail-DRT system so that passenger, infrastructure, and operational costs are minimized is described. The macroscopic model allows for studying the underlying relations between technological, operational and demand parameters, optimal capacity settings, and the obtained cost components. The model was applied to a series of numerical experiments to test its implications for different network structures and demand distributions. The results of the numerical experiments indicate that – unlike conventional rail systems in which stations often are capacity bottlenecks – link capacity properties are more critical for the performance of automated rail-DRT systems than station capacity. A series of sensitivity analyses was performed to test the consequences of various cost and capacity specifications, as well as the characteristics of future automated rail-DRT systems.