Flexible Transit Services Research Articles

Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach

Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.

Design a robust joint service of integrating demand responsive connectors (DRC) and shared bikes

ABSTRACT The integration of shared bikes and flexible transit services offers significant potential and benefits for urban transit systems. This paper presents a novel robust optimization framework for designing demand responsive connectors (DRC) that are fed by shared bikes. A two-stage model is formulated, with the first stage focusing on prereserved requests and the second stage addressing en-route random requests. The findings demonstrate that incorporating shared bikes can enhance the efficiency of serving en route late requests and reduce the required DRC vehicle fleet size. Additionally, it is observed that e-bikes are particularly suitable for this joint service due to their adaptability.

Offline planning and online operation of zonal-based flexible transit service under demand uncertainties and dynamic cancellations

This paper introduces a comprehensive framework for planning and operating a zonal-based flexible transit (FT) service, a public transit mode designed to accommodate uncertain demand patterns. The framework addresses both offline planning based on stochastic demand distributions and cancellations, as well as online routing considering real-time orders and cancellation behaviour. Offline interzonal route planning is formulated as a two-stage recourse problem, while the intrazonal routing problem is modelled using a Markov decision process (MDP) that incorporates online information. To solve the problem, a service reliability-based decomposition method is employed to divide the problem into three mixed-integer subproblems. The first subproblem focuses on designing interzonal routes up to a specific demand level, taking into account a designated cancellation probability as determined by reliability measures. An insertion heuristic is developed for this subproblem to improve the solution efficiency. The second subproblem allocates passengers from certain categories to vehicles based on the passenger volume designated by reliability measures. Lastly, the third subproblem refines the vehicle intrazonal route according to the passenger assignment from the previous subproblem. The reliability measures are optimised iteratively until no further improvements are observed in consecutive iterations. The proposed FT service’s performance is evaluated using numerical simulations based on real New York City (NYC) taxi demand data, illustrating the effectiveness of the integrated planning and operational approach in accommodating uncertainties in on-demand transit systems.

An adaptive route choice model for integrated fixed and flexible transit systems

Over the past decade, there has been a surge of interest in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modelling decision in the development is how one chooses to represent the mode- and route-choices of travellers. The real-time adaptive behaviour of travellers is important to model in the presence of a flexible transit service, where the routing and scheduling of vehicles is highly dependent on supply-demand dynamics at a near real-time temporal resolution. We propose a utility-based transit route-choice model with representation of within-day adaptive travel behaviour and between-day learning where station-based fixed-transit, flexible-transit, and active-mode alternatives may be dynamically combined in a single path. To enable experimentation, this route-choice model is implemented within an agent-based dynamic public transit simulation framework. We first explore model properties in a choice between fixed- and flexible-transit modes for a toy network. The adaptive route choice framework is then applied to a case study based on a real-life branched transit service in Stockholm, Sweden. This case study illustrates level-of-service trade-offs, in terms of waiting times and in-vehicle times, between passenger groups and analyzes traveller mode choices within a mixed fixed- and flexible transit system. Results show that the proposed framework is capable of capturing dynamic route choices in mixed flexible and fixed transit systems and that the day-to-day learning model leads to stable fixed-flexible mode choices.

Multi-Criteria Decision-Making Framework for Determining Public Microtransit Service Zones

The paper addresses a critical research gap in the literature by focusing on the identification of service areas for microtransit. It presents a framework for identifying areas where microtransit would have a higher potential for success. The primary objective is to assist agencies in their efforts to provide microtransit services by offering a robust and flexible decision-making framework. This framework serves as an intermediate step between an initial planning study that identifies candidate zones and a detailed feasibility study, which is currently missing in the literature. The framework is a multi-criteria decision-making (MCDM) process that can be applicable to various contexts. Specifically, our approach utilizes a lexicographic decision rule for ranking microtransit service priority measures for each census block of a selected region, such as a county. We employ two composite measures for ranking: Microtransit Propensity Index (MPI) and Weighted Accessibility Score (WAS). The latter combines transit access to work and to eight points of interest (POIs). This framework can be adapted to identify zones suitable for microtransit service based on different objectives, such as connecting residents to transit centers, providing first- and last-mile connections, providing services in transit deserts, providing flexible transit service to low-income communities, or providing access to the POIs within a zone. The effectiveness of the proposed framework is evaluated using the case of Prince George’s County in Maryland. The results reveal that our approach provides greater insights into the feasibility of an area for microtransit compared with relying solely on a single index such as MPI.

Integrated design framework for on-demand transit system based on spatiotemporal mobility patterns

On-demand transit is a flexible transit service designed to adjust the service schedule and route based on passengers’ dynamic demand. The operation of on-demand transit operates in accordance with physical and socioeconomic environments, and demand patterns. In order to meet the diverse mobility needs in urban areas, integrating different transit services is essential to improve both passenger convenience and operational efficiency simultaneously. We propose a data-driven design framework for an on-demand transit system that operates three types of services: planned-and-inflexible (PI), planned-and-flexible (PF), and unplanned-and-flexible (UF), each with varying levels of responsiveness to real-time demand. We classify historical demand data into three classes based on their spatiotemporal density. Then, we use the trip data of each class to plan and operate the PI, PF, and UF services. The performance of the proposed system is evaluated using real public transit data from Sejong city. Simulation studies reveal that the proposed system outperforms the existing on-demand transit system. Specifically, we observe that the PI and PF services, which are planned based on the historical spatiotemporal mobility patterns, highly compatible with requests that follow the major mobility patterns. At the same time, the UF service, which offers real-time routing without prior planning, covers areas and times beyond those served by the PI and PF services that do not correspond to major mobility patterns. Furthermore, we found that the proposed system is flexible enough to accommodate various real-world demand patterns by proving suggestions on the optimal vehicle operation for each service.

Data-Driven Flexible Vehicle Scheduling and Route Optimization

The flexible transit service reflects a trend of demand on the flexibility and convenience in urban public transport systems, within which the vehicle scheduling and passenger insertion are two challenging issues. Especially, finding the optimal solution for a flexible transit system can be viewed as an extension of the traveling salesman problem which is NP-complete. Yet most of the existing research mainly focuses on one aspect, i.e. route planning, stop selection or vehicle scheduling, where a combined integration and optimization of the whole system is largely neglected. In this paper, we propose a data-driven flexible transit system that integrates the origin-destination insertion algorithm and the milp-based (mixed-integer linear programming) scheduling scheme. Specifically, stops are mined from the historical datasets and some stops act as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$backbone$ </tex-math></inline-formula> stops that should be visited by the vehicles; and a heuristic backbone-based origin-destination insertion algorithm is proposed to schedule the routing path of vehicles, where the time loss caused by the optimal insertion positions is calculated for the vehicles to decide whether to accept the requests or not when constructing a path for the flexible routes. Moreover, a vehicle scheduling model based on milp is proposed to minimise the gap between the passenger flow and available seats. The proposed flexible transit systems are simulated in real-world taxi datasets, and experimental results show that the proposed flexible transit system can effectively increase the delivery ratio and decrease the passengers’ waiting time compared with existing methods.

Announcement System In Bus

Transportation in relatively congested urban areas has been the focus of transit firms. But as social economies have expanded, housing has become more scarce, demanding more flexible transit services. An odometer is a device used to figure out how far a vehicle has travelled. The car's dashboard is often where the odometer is found. The long-distance mode permits longer-distance serial communication between parallel processors through two I2C-bus controllers. When the serial interface is enabled (ESO = 1), choosing this mode requires setting ES1 to logic 1. The four unidirectional lines used to communicate the I2Cbus protocol in this mode are SDA OUT, SCL IN, SDA IN, and SCL IN (pins 2, 3, 4 and 5). The stop data, announcement audio, and advertisement elements for the bus route on Google Maps are all generated by this software. The generated information will be transmitted to the Announcer device through a Wi-Fi network with an internet connection. A diffuse reflective sensor is how an ultrasonic sensor typically operates. Compared to other sensor principles, it has better background suppression.

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.

A dynamic scheduling process and methodology using route deviations and synchronized passenger transfers for flexible feeder transit services

The general lack of first/last mile connectivity is one of the main challenges encountered by intercity transport systems. New transportation alternatives called flexible transit systems, which combine on-demand service adjustment capabilities to the regular route and master schedule characteristics of the conventional transit, are therefore increasingly proposed. Feeder transit services can run as one type of flexible transit services, connecting a service area to a major intercity transport network through a transfer hub. Considering the unstable and constantly changing characteristics of daily passenger demand, a dynamic scheduling methodology for feeder transit services is proposed to minimize passenger crowding in vehicles. Deviations in the base route are allowed for accommodating optional requests or visiting potential transfer locations, which further offer a new type of flexible scheduling operation called synchronized passenger transfer. Operators can dynamically expand the spatial coverage of vehicles out of their regular routes to meet each other at appointed locations and times. Then, passengers can transfer between vehicles to minimize or even avoid passenger crowding. In this study, a multi-commodity network flow optimization model with side constraints in a three-dimensional space–time–state network is proposed for this type of dynamic scheduling problem. Additionally, a coordinate descent-based solution framework consisting of three steps (augmentation, decomposition, and linearization) is developed. The proposed model and solution approach are investigated using an eight-node simple network and the Sioux-Falls network, and the Chicago sketch network was adopted to demonstrate the practicability of the proposed model.

A joint optimisation model for designing demand responsive connectors fed by shared bikes

ABSTRACT Demand Responsive Connectors (DRCs) have become a more general-purpose flexible transit service that caters to patrons’ personal needs. The traditional DRC operation, however, suffers from low efficiency due to excessive detours and incurs diseconomies of scale with respect to the demand and service area. To tackle this issue, we propose a novel DRC fed by shared bikes, which functions as an access/egress mode for certain request points. Analytical models are derived for the joint design of such a hybrid system. A mixed-integer non-linear programme is established to minimise the total system cost. A heuristic solution algorithm is developed by combining the simulated annealing and branch-and-bound algorithms. A series of numerical cases are designed to evaluate the proposed system’s performance against the traditional ones. The results demonstrate that the introduction of shared bikes can reduce the DRC tour length and consequently save total system costs.

Improving the performance of first- and last-mile mobility services through transit coordination, real-time demand prediction, advanced reservations, and trip prioritization

Socio-demographic trends and recent economic development patterns have resulted in travel behavior changes that call for more flexible and accessible public transit options. Because flexible transit services vary in scope, size, and service type, new data-informed methods are useful to optimize services based on the specific needs of local communities and riders. In this study, real-world demand and vehicle trajectory data were used to evaluate and optimize system performance for an existing first-mile–last-mile (FMLM) service in Robinson Township, PA. A general FMLM model for arbitrary demand and service supply was then developed to quantify system performance—both travel time costs and day-to-day reliability—for various operational polices considering spatio-temporal demand variation and transportation network dynamics. Heuristics were used for optimal real-time vehicle routing in sizable real-world networks accommodating various service types and scopes. In this case study, total user costs were reduced by 18.6% when rides were coordinated with mainline fixed-route transit. Predictive routing strategies were shown to marginally improve system performance under sparse and variable spatio-temporal demand. The case study also highlights potentially large travel time and user reliability improvements—reductions of 51% and 53.8%, respectively—when trip requests were made in advance of their desired pickup time. Finally, we show that travel time reliability can be improved for time-inflexible trips with trip prioritization without increasing total user costs. These results were stable to changes in demand density.

Optimizing dynamic switching between fixed and flexible transit services with an idle-vehicle relocation strategy and reductions in emissions

Abstract A novel approach to dynamic switching service design based on a new queuing approximation formulation is introduced to systematically control conventional buses and enable provision of flexible on-demand mobility services. This new approach guides smart vehicles in a service area that needs last mile transit services via either traditional buses, which provide fixed-route services, or flexible-route on-demand mobility services. We also include dynamic pre-positioning of idle vehicles in anticipation of new customer arrivals, and relocation of vehicles to rebalance the use of vehicles in the system, which can have a sizable effect on energy and environmental conservation. Using a New York City transit dataset, the proposed strategy for non-myopic switching between flexible-route and fixed-route service and re-positioning of idle vehicles improves social welfare by up to 32%, while the impact of the proposed strategy on vehicle miles traveled is shown to be as high as 53% over that of the current transit service.

A Methodology for Choosing between Route Deviation and Point Deviation Policies for Flexible Transit Services

Flexible transit services, which bring together the characteristics of fixed-route transit and demand-responsive transit, have been proven to be cost-efficient in low-density residential areas. In this paper, a methodology is proposed to assist planners in making better decisions when choosing between route deviation policy and point deviation policy, which are two promising types of flexible transit services. A user cost function is developed to measure the service quality of the transit systems, and analytical models are constructed to compare the system performance under both expected and unexpected demand levels. Based on the experiments for various scenarios over a real-life transit example, the critical demands, which represent the switching point between the two competing service policies, have been derived. Our findings show that point deviation policy is more efficient at low-demand levels, while route deviation policy is a better choice at low-to-moderate demand levels. At unexpectedly high demand levels, route deviation policy is better able to accommodate rejected passengers than point deviation policy.

Riders on the storm: Exploring weather and seasonality effects on commute mode choice in Chicago

This paper separately analyzes the impacts of weather and seasonality on commute mode choice using a stated-preference (SP) survey. The study’s twin objectives include quantifying the relative attractiveness of a flexible transit mode under adverse weather and analyzing the heterogenous impacts of seasonality and weather on different segments of the population. The survey was implemented during summer and winter months to analyze the impact of seasonality on commute mode choice. To analyze the impact of weather on commute mode choice, the survey randomly assigns respondents to a good or bad weather scenario. The three SP mode choice options are fixed-route transit, personal car, and a hypothetical flexible transit mode. The flexible transit mode allows travelers to wait for a transit vehicle at their origin point – an especially attractive feature on cold and/or snowy/rainy days. However, like with existing dial-a-ride services, travelers using the flexible transit service may experience detours between their pickup and drop-off locations. Several panel mixed-logit choice models that incorporate weather and seasonality were estimated using the SP survey data. The panel mixed-logit model form captures taste heterogeneity across respondents and correlation across multiple SP choice experiments from a single respondent.Model estimation results indicate that relative to good weather, summer days, respondents were significantly more likely to choose traditional and flexible transit on good weather, winter days, and flexible transit on bad weather, winter days. Furthermore, the results show that the impacts of weather and seasonality on commute mode choice vary across the population. Most of the respondents had a lower (higher) propensity to choose the car (flexible transit) mode under bad weather than good weather. Conversely, car commuters, bicycle commuters, and baby boomers had a much higher propensity to choose the car mode under bad weather than good weather. Additionally, the results show that millennials had a much lower propensity to choose the car mode in the winter than the summer, whereas seasonality had little impact on the propensity of non-millennials to choose the car mode. The paper discusses the implications of these findings on the design of transit, flexible transit, and emerging mobility services, as well as the management of transportation systems and dissemination of information regarding alternative transport modes during adverse weather events.

User perspectives on emerging mobility services: Ex post analysis of Kutsuplus pilot

Kutsuplus was a novel, flexible micro transit service (FMTS) operating in Helsinki during 2012 to 2015. The service included a range of new technological development, ranging from routing algorithm to marketing and user interface. However, at the end of 2015, the service ceased due to budgetary constraints. In the context of service discontinuation, and the lack of in-depth understanding of user perspectives about urban FMTS, this paper aims to uncover the perspectives of the users of the service, users that discontinued using the service during its operation, and persons who did not use the service. The methodological approach is based on a questionnaire, with mapping capabilities enabling collection of georeferenced data. Questionnaire results are validated using actual Kutsuplus trip analysis. The results show that Kutsuplus users were a diverse group both when considering socio-economic status and travel behavior. In addition, the results include detailed analysis of stated trip characteristics, including spatial analysis of trip origins and destinations. Furthermore, the results include qualitative analysis of respondents' opinions and recommendations about positive and lacking FMTS features. The paper ends with a summary of positive Kutsuplus features, followed by the discussion of aspects for future deployment, including end-user and service area analysis, marketing strategy, and service usability. Finally, the paper provides recommendations for further research on FMTS.

Mining taxi trajectories for most suitable stations of sharing bikes to ease traffic congestion

Sharing bike service is a new emerging public transportation, which has been the hottest topic for months. Sharing bike service provides flexible demand-oriented transit services for city commuters. However, as large amount of sharing bikes flood into big cities, problems caused by chaotic order of sharing bikes are emerging slowly. The authors aim to draw support from taxi trajectories to analyse current traffic condition and improve it with sharing bikes. In this study, the authors propose a traffic congestion finding framework, called CF. In CF, derived from the points density-based clustering method of inspiration, the authors propose a new clustering method (CF-Dbscan) and successfully applied it to the clustering of trajectories. A road network matching algorithm (CF-Matching) helps to match GPS points to road net even if points are in low-sampling-rate. They also employ a ranking feedback mathematical model to adjust the number of sharing bikes of different stations to meet people's demand and reduce redundancy. The first experiment proves that the proposed clustering algorithm performs better than traditional DBSCAN. Another experiment is conducted to verify the effectiveness of the proposed framework in reducing traffic congestion. The experimental results prove that with the proposed framework the authors can achieve the purpose of easing traffic congestion.

Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options

The first analytical stochastic and dynamic model for optimizing transit service switching is proposed for “smart transit” applications and for operating shared autonomous transit fleets. The model assumes a region that requires many-to-one last mile transit service either with fixed-route buses or flexible-route, on-demand buses. The demand density evolves continuously over time asan Ornstein-Uhlenbeck process. The optimal policy is determined by solving the switching problem asa market entry and exit real options model. Analysis using the model on a benchmark computational example illustrates the presence of a hysteresis effect, an indifference band that is sensitive to transportation system state and demand parameters, as well as the presence of switching thresholds that exhibit asymmetric sensitivities to transportation system conditions. The proposed policy is computationally compared in a 24-hour simulation to a “perfect information” set of decisions and a myopic policy that has been dominant in the flexible transit literature, with results that suggest the proposed policy can reduce by up to 72% of the excess cost in the myopic policy. Computational experiments of the “modular vehicle” policy demonstrate the existence of an option premium for having flexibility to switch between two vehicle sizes.

Vehicle routing and scheduling of demand-responsive connector with on-demand stations

As an important supplement of conventional fixed-route public transit, flexible transit services draw more attention in low-demand areas recently. Demand-responsive connector is one of those flexible transit services and has already been operated as feeder transit in some cities. This article concentrates on the vehicle routing operation of the demand-responsive connector system with on-demand stations. A two-stage routing model is proposed to minimize the system cost, considering both the service provider and riders, and two solution algorithms are proposed and compared for the routing model. A simulation experiment based on a real-life case study in Nanjing city is conducted to demonstrate the applicability of the proposed vehicle routing model. The results demonstrate that our vehicle routing modeling and optimized algorithm can confidently handle the daily operation of demand-responsive connector services with on-demand stations.

Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options

Abstract: The first analytical stochastic and dynamic model for optimizing transit service switching is proposed for “smart transit” applications and for operating shared autonomous transit fleets. The model assumes a region that requires many-to-one last mile transit service either with fixed-route buses or flexible-route, on-demand buses. The demand density evolves continuously over time as an Ornstein-Uhlenbeck process. The optimal policy is determined by solving the switching problem as a market entry and exit real options model. Analysis using the model on a benchmark computational example illustrates the presence of a hysteresis effect, an indifference band that is sensitive to transportation system state and demand parameters, as well as the presence of switching thresholds that exhibit asymmetric sensitivities to transportation system conditions. The proposed policy is computationally compared in a 24-hour simulation to a “perfect information” set of decisions and a myopic policy that has been dominant in the flexible transit literature, with results that suggest the proposed policy can reduce by up to 72% of the excess cost in the myopic policy. Computational experiments of the “modular vehicle” policy demonstrate the existence of an option premium for having flexibility to switch between two vehicle sizes.