Customized Bus Research Articles

Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models

This paper proposes customized bus (CB) demand models to investigate dynamic adjustments, spatial dependence, and spatial spillover effects using spatial dynamic panel data techniques and a balanced panel data set collected over two years. The endogeneity introduced by spatial dependence effects is resolved by extending the moment restrictions of the system GMM estimator to a spatial autoregressive dynamic panel. A wide range of variables representing factors related to 1) characteristics of service supply, 2) demographic characteristics, and 3) land use and accessibility were mined and fused to investigate their direct and spillover effects on CB demand. The results reveal the mechanism of spatial dependence such that the increments (or decrement) in demand rather than the amount of demand itself in the neighbourhood contribute to the spatial dependence effects. There is more than 43% matched demand generated in a given area for each-unit increment of demand from its neighbourhood. CB services are more attractive for passengers with long-distance trips, and therefore, increasing the focus on long-distance trips is one potential strategy for increasing ridership. Determined by the role of CB in the transportation system, areas with poor accessibility are found to have larger market niches for CB services.

Integrated Optimization for Commuting Customized Bus Stop Planning, Routing Design, and Timetable Development With Passenger Spatial-Temporal Accessibility

The customized bus (CB) is an innovative type of transit service that can provide a personalized efficient transit services for passengers and environmental friendliness and congestion alleviation in metropolitan areas. This work develops an integrated optimization method for CB stop deployment, route design, and timetable development optimization problems while meeting travel demands as much as possible to obtain system-optimal CB service plans. Through the perspective of space-time network, the CB service design problem (CBSDP) is formulated as an integrated optimization model with the objectives of maximizing passenger accessibility and minimizing operating cost. An inconvenience index of passengers is introduced in the problem to measure the service quality, and the total number of stops for all involved CB routes is set as one of the objectives to optimize the total cost of the CB system. A heuristic approach is applied to generate efficient solutions for the CBSDP. Two types of instance, namely, a numerical experiment and a real-world instance, are implemented to demonstrate the performance of the proposed method. We also conduct a series of sensitive analyses to explore the influences of various parameters on the CB system for capturing the interaction among stops, routes, and timetables. Final results show that the CB plans obtained by the proposed method can provide efficient services by balancing passenger convenience and operating cost.

Integrating Passenger Incentives to Optimize Routing for Demand-Responsive Customized Bus Systems

The customized bus (CB) is an alternative public transportation mode that extends the flexibility and coverage of the fixed-route transit networks. It allows passengers to make reservations for trips and arranges vehicles to serve shared rides. However, the operation performance is limited to vehicle routing, the number of stops, and the length of detour times. Extra detours and stops would not be significantly avoided if deploying high-capacity vehicles. The demand control aspect is a possible way out. Releasing incentives to passengers can attract them to aggregated locations and reduce the vehicle detour times. How to determine an appropriate incentive scheme for passengers is the critical problem. This paper presents an approach to integrating the disaggregated trip choice model with the vehicle routing model to determine incentive schemes. First, the discrete choice model is established to bridge the passengers' trip choice probabilities with the influence of monetary incentives, walking time, and travel time. A vehicle routing model based on the pickups and deliveries problem is then adopted to generate the routes and schedules of vehicles to serve the influenced passengers. The result shows that the proposed approach can reduce the total running kilometers, shorten the onboard time, and increase the profits. The analysis also suggests that passengers' sensitivity towards incentives is decisive to the result.

Cross-Regional Customized Bus Route Planning Considering Staggered Commuting During the COVID-19.

In order to solve the problem of cross-regional customized bus (CB) route planning during the COVID-19, we develop a CB route planning method based on an improved Q-learning algorithm. First, we design a sub-regional route planning approach considering commuters’ time windows of pick-up stops and drop-off stops. Second, for the CB route with the optimal social total travel cost, we improve the traditional Q-learning algorithm, including state-action pair, reward function and update rule of Q value table. Then, a setup method of CB stops is designed and the path impedance function is constructed to obtain the optimal operating path between each of the two stops. Finally, we take three CB lines in Beijing as examples for numerical experiment, the theoretical and numerical results show that (i) compared with the current situation, although the actual operating cost of optimized route increases slightly, it is covered by the reduction of travel cost of passengers and the transmission risk of COVID-19 has also dropped significantly; (ii) the improved Q-learning algorithm can solve the problem of data transmission lag effectively and reduce the social total travel cost obviously.

Exploring Observed and Unobserved Preference Heterogeneity in Choice Behavior of Demand Responsive Customized Bus Service

Abstract The users' taste preferences for the demand responsive customized bus (DRCB) and, in particular, the its mechanism of heterogeneity, which determine routing, timetabling, and even launch a...

Planning Bus Transit Unit in Sprawling Townships; Khurdha Road Junction, Odisha; India

The customized buses (CBs) is a novel approach of community transportations at present and have become popular mode of expanded municipal conveyance, modernized, gorgeous and traffic services. The CB is planned by combining long term demand and passenger’s comforts and necessities. Based on analysis of the passengers travel data from inland and abroad at a focused point, the development of the CB is to be planned and proposed in a small town like Khurdha Road Junction (Jatni) in Odisha.. Present study points out the glitches linked with the operation and maintenance, expansion of CBs depending upon increase in numbers of travelers and other factors like stop assortment, line plan, schedules, and the impact of the proposed new public bus transit system. Traffic excellence factors, like average speed, delays, traffic jamming, travel time, and cost were considered while planning for the new transit bus terminal. The small towns around the smart city Bhubaneswar is gazing at a grave commuting conundrum. The planning and construction of the bus transit at Khurdha Road junction (Jatni) is developed to cope with the sprawling township and save the roads from severe traffic jam. Economic analysis with environmental Impact assessment of the project is done.

Customized bus route design with pickup and delivery and time windows: Model, case study and comparative analysis

The customized bus (CB) is an emerging type of public transportation system, which not only provides a flexible and reliable demand-responsive service, but also reduces the usage of private car to alleviate traffic congestion in metropolitan cities. The customized bus route design problem (CBRDP) is a crucial procedure in the CB service system designing. In this work, we develop a new type of problem scenario: Multi-Trip Multi-Pickup and Delivery Problem with Time Windows, to describe CBRDP by simultaneously optimizing the operating cost and passenger profit, where excess travel time is introduced to estimate passenger extra cost compared with taxi service, and each vehicle is allowed to perform multiple trips for operational cost savings. To solve this problem, a constructive two-stage heuristic algorithm is presented to obtain the Pareto solution. Taking a benchmark problem and Beijing commuting corridor as case studies, we calculate and compare the monetary and travel costs of CB with other travel modes, and quantitatively confirm that the CB can be a cost-effective choice for passengers.

Time-Dependent Urban Customized Bus Routing With Path Flexibility

Urban customized bus companies are increasingly motivated by design efforts that entail more efficient route scenarios to incorporate adaptation to temporal and spatial heterogeneity in travel demand. However, such motivations are usually hindered by ubiquitous arrival unpunctuality resulting from traffic congestion. To resolve this problem, we suggest a time-dependent bus route planning methodology that explicitly considers path flexibility between nodes to be visited. First, we establish a mixed-integer programming model to formulate the problem, where decision-making considerations in bus route planning, path choice between nodes, and passenger assignment are concurrently integrated. Then, we develop a hybrid metaheuristic (combining tabu search and variable neighborhood search) to solve the model, in which satisfactory performance is observed from the numerical test in a small-sized example. Finally, the problem and methodology are addressed in a city-scale instance, where the effects of time-window features and traffic congestion, as well as the benefits from path flexibility inclusion in terms of cost, travel time, and distance are investigated.

A Multi-Objective Robust Optimization Model for Customized Bus Routes

Various customized bus route optimization methods based on certain conditions have been applied to the actual route optimization problems, but the actual operation process of customized buses mostly lies in an uncertain condition. In this paper, a three-stage hybrid coding method based on NSGA-II algorithm was proposed to deal with customized bus route optimization under uncertain condition. Firstly, with the objective of minimizing passenger travel time and customized bus carbon emission, a robust optimization model was constructed. Second, with the Bertsimas-Sim robust optimization theory, the robust peer-to-peer transformation was performed on the robust model with uncertain parameters. Finally, the practical issue including three customized bus parking lots and 20 boarding and alighting stations were solved to verify the rationality of the model and algorithm. Compared with the hybrid algorithm based on K-means and multi-objective genetic algorithm, this method reveals not only better solution results, but saves 42.11% of computing time. The results are of great value for exploring customized bus route optimization methods and improving the efficiency of customized bus operations.

Customized Bus Route Optimization with the Real-Time Data

This paper investigates the real-time customized bus (CB) route optimization problem, which aims to maximize the service rate for clients and profits for operators. The on-road bus has a flexible route, which can be updated based on the real-time data and route optimization solutions. A two-phase framework is established. In phase 1, the vehicle-related data including existing route and schedule, client-related data involving pick-up/drop-off location, and time windows are collected once receiving a new CB request. The second phase optimizes the bus route by establishing three nonlinear programming models under the given data from phase 1. A concept of profit difference is introduced to decide the served demand. To improve computation efficiency, a real-time search algorithm is proposed that the neighboring buses are tested one by one. Finally, a numerical study based on Sioux Falls network reveals the effectiveness of the proposed methodology. The results indicate that the real-time route optimization can be achieved within the computation time of 0.17–0.38 seconds.

Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration

Employment is one of the essential socioeconomic connections in urban agglomeration. However, both the demand and the supply of transjurisdictional public transport service are unevenly distributed in such area. Customized bus has a high potential of serving transjurisdictional and long-distance commuting demand. This study proposes a network-based layout design method to generate hierarchical service scopes and stations for customized bus system. First, the home and the workplace of residents are identified using mobile phone data, to construct a jobs-housing relationship network. Then, an iterative algorithm based on network community detection and density-based spatial clustering is applied to the jobs-housing relationship network to hierarchically segment the urban agglomeration area into communities. Three methods are proposed for location selection of customized bus stations. A case study is conducted using the mobile phone dataset from nine cities in the Yangtze River Urban Agglomeration in China. A four-level hierarchical customized bus system layout is generated and both the spatial properties and network properties of service scopes are analyzed. The proposed three methods of customized bus station location selection are compared based on the average travel distance and the rationality of the resulted customized bus station location.

Operational strategy of customized bus considering customers’ variety seeking behavior and service level

In recent years, customized bus (CB) is becoming an innovative model of the public transport system (PTS) in China. This service provides an advanced, timely, personalized, and flexible responses to the demand of the PTS, especially of commuters. Obviously, it could have an impact on public transport (PT) service. For instance, CB services influence the customers' variety-seeking behavior, which results in changes of customer options in different periods. Therefore, customers could be divided into different groups of preference. In this paper, we construct a two-period Hotelling game model by variety-seeking behavior and service level. The results show that, in the first stage, the departure frequency of CB and PT is influenced by variety-seeking behavior and service level. In the second stage, departure frequency is influenced by service level only. Next, although the departure frequency of bus impacts the changes of passenger ratios in different stages, service level appreciably impacts passenger number. From customer surplus and social welfare, we find that adjustment of service level can increase social welfare, which means that CB and PT will reach an optimum effect. The present work can provide a valuable reference to policymakers, practitioners, and others.

Multi-objective optimization of customized bus routes based on full operation process

In order to rationally formulate a customized bus route plan to improve the operation efficiency of customized buses, in view of the problem of ignoring the distance between the boarding area and the alighting area, and setting it as a fixed value when performing custom bus route optimization modeling, as well as solving the problem of multiple custom bus parking lots. This paper proposes a method based on the NSGA-II algorithm using a three-stage hybrid coding method to solve. First, according to real life, the entire operation process of customized buses is divided into four phases. Second, based on the four stages, a multi-objective optimization model of customized bus routes that satisfies multiple parking lots, multiple cars, and multiple boarding and alighting stations is constructed to pursue the minimum total travel time of passengers and minimizes the operating costs of customized bus companies. Third, the NSGA-II algorithm is employed to solve the model, including three-stage hybrid coding, segmented crossover, and segmented mutation operators. Finally, the local road network in Lanzhou City is adopted for simulation research. The results show that it is feasible to use NSGA-II algorithm to solve the problem of customizing bus routes for multiple parking lots, multiple vehicles, and multiple boarding and alighting stations. The research results are of great value for exploring the optimization methods of customized bus routes and improving the operating efficiency of custom buses.

Key determinants and heterogeneous frailties in passenger loyalty toward customized buses: An empirical investigation of the subscription termination hazard of users

Long-term passenger subscription is vital to the survival and operation of the customized bus (CB) system, which is a demand-driven and user-oriented transit service. A better understanding of passenger loyalty toward the CB service will help provide better operation. The urgent and outstanding issue is how to incorporate the unobserved heterogeneity in loyalty—in other words, how to reflect the effects of the frailty to terminate subscription. This study fills the research gap through an empirical study in Dalian, China. Three different survival models are developed to investigate the mechanism of subscription behaviors, among which the shared frailty model considering the unobserved heterogeneity is demonstrated to be the most appropriate. The results indicate that the historical purchase characteristics are the most important to CB user loyalty modeling and forecasting. Males are more sensitive than females to the number of intermediate stations because of the potentially increased uncertainty in waiting time related to the intermediate stations. The heterogenous frailties resulting from the heterogeneity of the perceptible service quality in terms of convenience and efficiency in subscribing/returning tickets and information availability in the progress of the CB system significantly contribute to user loyalty deviations.

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

The customized bus (CB) transit is recognized as an effective transportation mode offering more flexible and demand-responsive service than traditional bus transit with fixed route and schedule, especially during the peak hours. The novelty of this study is the development of a mixed integer non-linear model for optimizing multi-terminal CB service in an urban setting. According to the estimated spatiotemporal passenger demand, the objective total cost, consisting of supplier’s and users’ costs, is minimized subject to capacity and time window constraints. A mixed bus fleet with various bus sizes is employed to accommodate passenger demand, which increases vehicle utilization and reduces supplier’s cost. The inconvenience of passengers caused by early arrival at the destination is treated as penalty and considered in users’ cost. The study optimization problem is combinatorial with many decision variables including trip assignment, bus routing and associated timetables, and fleet size. A hybrid genetic algorithm (HGA) which integrates the features of genetic algorithm (GA) and simulated annealing (SA) is developed to effectively search for the optimal solution. A real-world CB network is employed to demonstrate the applicability of the developed model and explore the relation between the model parameters and optimized results. It was found that the total cost can be reduced by 16.5% after employing multiple terminals and a mixed bus fleet.

改进的A*算法在定制公交路径规划中的应用

改进的A*算法在定制公交路径规划中的应用

A two-phase optimization model for the demand-responsive customized bus network design

This paper proposes a new optimization model for the network design problem of the demand-responsive customized bus (CB). The proposed model consists of two phases: inserting passenger requests dynamically in an interactive manner (dynamic phase) and optimizing the service network statically based on the overall demand (static phase). In the dynamic phase, we propose a hierarchical decision-making model to describe the interactive manner between operator and passengers. The CB network design problem is formulated in a mixed-integer program with the objective of maximizing operator’s revenue. The CB passenger’s travel behavior is measured by a discrete choice model given the trip plan provided by the operator. A dynamic insertion method is developed to address the proposed model in the dynamic phase. For the network design problem in the static phase, the service network is re-optimized based on the confirmed passengers with strict time deviation constraints embedded in the static multi-vehicle pickup and delivery problem. An exact solution method is developed based on the branch-and-bound (B&B) algorithm. Numerical examples are conducted to verify the proposed models and solution algorithms.

Factor Analysis of Customized Bus Attraction to Commuters with Different Travel Modes

The customized bus (CB) is an innovative and environmental supplementary mode of public transport, providing demand-responsive and user-oriented service to specific passenger groups with similar travel demands, especially commuters, based on online reservations. However, sufficient travel demand is essential for the successful operation of CB. The purpose of this study is to analyze the factors influencing the attraction of CB to commuters, which is tied to the ordered mode shift decisions, do no transfer to CB, remain undecided, and transfer to CB. A combination of revealed preference (RP) survey and stated preference (SP) survey is conducted among commuters in Beijing through online and offline questionnaire, collecting 1304 valid commuting demands. The ordered logit (OL) model and two-level mixed-effect ordered logit (MEOL) model are used to estimate the variable effects and the difference in five commute modes, including car, taxi, bus, rail, bus + rail, is considered. Common variables significantly influencing the transfer decision in all groups are specified in models, including familiarity to CB, seat availability, and gender. Meanwhile, travel cost, travel time, and transfer time of the current travel mode have positive effects on the attraction of CB. In addition, car ownership and accessibility to bus stations also influence the attraction of CB to certain group commuters. This paper can provide references to CB operators for formulating differentiation strategies and attracting more passengers in Beijing.

Customized Bus Network Design Based on Individual Reservation Demands

With the advantages of congestion alleviation, environmental friendliness, as well as a better travel experience, the customized bus (CB) system to reduce individual motorized travel is highly popular in increasing numbers of cities in China. The line planning problem is a key aspect of the CB system. This paper presents a detailed flow chart of a CB network planning methodology, including individual reservation travel demand data processing, CB line origin–destination (OD) area division considering quantity constraints of demand in areas and distance constraints based on agglomerative hierarchical clustering (AHC), an initial set of CB lines generating quantity constraints of the demand on each line and line length constraints, and line selection model building, striking a balance between operator interests, social benefits, and passengers’ interests. Finally, the impacts of the CB vehicle type, the fixed operation cost of online car-hailing (OCH), and the weights of each itemized cost are discussed. Serval operating schemes for the Beijing CB network were created. The results show that the combination of CB vehicles with 49 seats and 18 seats is the most cost-effective and that CBs with low capacity are more cost-effective than those with larger capacity. People receive the best service when decision-makers pay more attention to environmental pollution and congestion issues. The CB network’s service acceptance rate and the spatial coverage increase with the fixed operating cost per OCH vehicle per day c 0 C . The CB vehicle use decreases as c 0 C c c increases. The results of this study can provide technical support for CB operators who design CB networks.

Multi-objective optimization of real-time customized bus routes based on two-stage method

Customized public transit, as a diversified passenger service mode proposed under the new situation, not only deals with the comfortable and convenient commuting needs of most commuters, but also improves the efficiency of road use effectively and alleviates the road traffic pressure in the morning and evening rush hours. Currently, various routing optimization methods based on static passenger requests have been applied to the optimization design of customized bus routing in major cities, but the static passenger requests cannot satisfy the dynamic passenger requests of random users at present Therefore, it is urgent and necessary to investigate real-time customized bus routing optimization methods based on static passenger requests and dynamic passenger requests. In this paper, our goal is to present an approach to tackle two important issues of real-time customized bus route optimization under stochastic user demand: one is that how to deal with dynamic travel requests in a specific road network to improve passengers’ satisfaction with customized bus services; the other is that how to employ specific methods to achieve real-time updating of customized bus routes to meet the random user’s travel requests. Starting from the whole operation process of customized bus, we firstly construct a multi-objective optimization model of customized bus route, and clearly depicts the four processes of customized bus operation in the form of mathematical expressions. Second, we design a two-stage method based on NSGA-II algorithm to solve the model, in which the first stage deals with static bus travel requests to obtain the initial customized bus route optimization scheme, and the second stage deals with dynamic bus travel requests to update the customized bus route in real time. Finally, a two-stage case is illustrated to verify the correctness and validity of the model and the method for solving the real-time customized bus route optimization problem under stochastic user demand.