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For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs. [ABSTRACT FROM AUTHOR]

• Substantial surveys identify that bus queuing, merging, and repeated opening doors can significantly affect bus dwell time. • A Markov chain is designed to characterize the effect of vehicles in adjacent lane and passengers on bus merging. • A comprehensive model is proposed to estimate bus dwell time and bus queuing time. • The actual performance of the comprehensive model is validated in real cases in three cities. • The probability of bus overtaking is highly correlated with bus service time and average stop service time. The precise estimation of bus dwell time (BDT) is immensely important as it directly impacts the reliability of bus schedules and the accuracy of arrival time predictions. Various factors—such as the behavior of buses merging into the main traffic flow, uneven distribution of passengers on board, and frequent bus queues—jointly lead to a high variability of the BDT. To achieve an accurate estimation of BDT across various stop types, a comprehensive BDT model is developed in this paper. First, based on berth occupancy, probabilistic models are constructed for different bus arrival scenarios, which are then used to study corresponding bus queuing times. Second, the Bureau of Public Roads function is employed to characterize the obstruction effect occupants on the bus impose on boarding passengers. Combined with this function, a bus service time model is proposed considering the bus opening doors more than once and in-vehicle crowding. Third, the bus merging process is discretized to cope with the volatility of the time interval required for a bus to leave the bus bay. A Markov chain model is designed to calculate the time buses have to wait before merging. Case studies conducted in Qingdao, Chongqing and Beijing, China, demonstrate the effectiveness of the comprehensive model both during off-peak and peak periods. Furthermore, this paper incorporates overtaking rules into the comprehensive model to examine the influence of bus overtaking willingness and various overtaking policies on bus queuing times. Through numerical analysis, the key link is between willingness to overtake, service time and the likelihood of bus overtaking is disclosed. [ABSTRACT FROM AUTHOR]

Concerns over traffic congestion and environmental pollution have prompted more studies into the impacts of built environment on commuters' mode choice behavior. This paper aims to make a comprehensive analysis of commuters' mode choice behavior with the 5th Household Travel Survey in Beijing. A mixed nested logit model was employed to jointly explore the impacts of mode attributes and built environment variables on commuters' mode choice. The results show that the proposed mixed nested logit model outperforms the contrastive models, i.e., standard multinomial logit model and nested logit model. Estimated results indicate that non-motorized mode commuters pay more attention to OD distance. Land-use mix and density have significant influence on reducing car preference, and only show significant effect in working zone. Furthermore, public modes including bus and subway are regarded as one group that the convenient of either one of them changes will influence commuters' preference on the whole public transportation system. [ABSTRACT FROM AUTHOR]

Road disruptions frequently occur in practice, resulting in a productivity loss of bus services and widespread passenger delays. These negative impacts are significant, especially when multiple routes are influenced at the same time. In order to mitigate these impacts, this paper proposes a multi-route coordination approach that collaboratively adjusts multiple bus routes and optimizes bus timetables to provide effective alternative bus services for passengers. Three adjusting strategies are adopted for bus routes with varying passenger demand: detouring, short-running, and cancellation. To address the multi-route coordination problem, a column-generation-based two-stage framework is developed. Concretely, a column generation technique is utilized in the first stage to iteratively generate candidate adaptive bus routes and passenger paths. Following that, an integrated integer programming model is built in the second stage to simultaneously determine bus timetables and the combinations of those adaptive bus routes. After realizing the low computational efficiency for large-scale problems, this paper designs a customized decomposition algorithm based on set partitioning to solve the presented model and obtain near-optimal solutions efficiently. Finally, the proposed methodology is applied to an illustrative Sioux Falls network and a real-world bus network in Beijing to verify its validity and effectiveness. Three comparative analyses are conducted to discuss the advantages of the three adjusting strategies, to investigate the benefits of coordinating bus timetables, and to explore the applicability of different adjusting strategies, respectively. • A multi-route coordination problem for bus systems in response to road disruptions. • A column-generation-based two-stage framework to address the problem. • A customized decomposition algorithm based on set partitioning for model solving. • The framework and algorithm are applied to a real-world bus network in Beijing. [ABSTRACT FROM AUTHOR]

In order to tackle the congestion and environmental issues, customized bus services are proposed and deployed in metropolitan areas. As emerging public transportation services, customized bus services bring passengers more convenience and accessibility. Besides, conventional customized bus services generally organize homogeneous fleet and single location selection to passengers. In this paper, to enhance the mobility and flexibility of customized buses and increase companies' profit, we propose a new form of customized bus service with heterogeneous fleets and multiple candidate locations. First, a mixed-integer programming model (MIP) is developed to describe the customized bus problem. Compared with the conventional model, the proposed MIP is involved in the case of one passenger with multiple candidate pickup or delivery locations and can be solved by GUROBI on small scale, quickly and efficiently. Second, an improved adaptive large neighborhood search algorithm ( ALNS i p ) is utilized to address the large-scale problem more efficiently. Time slack calculation method is then designed to optimize vehicle timetables, which provides stable and excellent performance for searching feasible solutions. In addition, we propose two inserting operators to deal with the problem with multiple candidate locations and analyse its influence on the results. Finally, we test the performance of the proposed model and algorithm on the numerical experiments. And they are verified the effectiveness and implication in a small-scale case on a simplified Sioux waterfall network and a large-scale problem in Beijing, China. The result shows that ALNS i p outperforms other algorithms in searching for more satisfying solutions with higher efficiency. However, the GUROBI solver can obtain the solution to small-scale problems within a shorter time than ALNS i p . Furthermore, it can be suggested that the heterogeneous fleets service with multiple candidate locations is helpful to facilitate collaboration among vehicles and optimize pickup and delivery routes in consequence. [ABSTRACT FROM AUTHOR]

• A bus bridging approach to cooperate with the first train operations in a subway network. • An explicitly integrated MILP model for the first train and bus bridging timetabling problems. • A tailored algorithm for optimally solving the first train timetabling and bus service bridging problems. • The model and the approach are applied to the Beijing subway network. Subway system is the main mode of transportation for city dwellers and is a quite significant backbone to a city's operations. One of the challenges of subway network operation is the scheduling of the first trains each morning and its impact on transfers. To deal with this challenge, some cities (e.g. Beijing) use bus 'bridging' services, temporarily substituting segments of the subway network. The present paper optimally identifies when to start each train and bus bridging service in an intermodal transit network. Starting from a mixed integer nonlinear programming model for the first train timetabling problem, we linearize and reformulate the model using the auxiliary binary variables. Following that, the bus bridging model is developed to cooperate with the first train operation for reducing long transfer waiting times. After realizing the low computational efficiency of solving the integrated model, a tailored algorithm is designed to optimally solve the first train timetabling and bus service bridging problems. The exact models and algorithms are applied to the Beijing subway network to test their effectiveness and computational efficiency. Numerical results show that our approaches decrease the total passenger waiting time by 53.4% by a combined effect of adjusting the first train departure times and operating 27 bridging buses on 7 routes. [ABSTRACT FROM AUTHOR]

With the development of social economy and health care technology, people are expected to live longer, and the world's population tends to be ageing. As one of the services for elderly people, the concessionary bus travel programme provides a kind of social welfare for elderly people, while peak-hour traffic pressures (e.g., traffic congestion, traffic injuries, and travel discomfort) faced by elderly people have been ignored. This paper attempts to help society create a better travel environment for elderly people by analysing their travel behaviours. Specifically, using household survey data from Beijing, the statistical analysis is used to paint a more detailed picture of daily travel experience of elderly people with regard to departure times, travel purposes, travel mode choices, etc. Then, a nested logit model is employed to interpret travel patterns of elderly people. The results show that the bus plays the most important role in motorized travel of elderly people. Furthermore, the free bus programme has induced only 4.85% of bus travel among elderly people, indicating that the programme has little effect on elderly people's bus choices. Based on the results of the analysis, several options are proposed in this paper to help society adapt to the changing demographics. [ABSTRACT FROM AUTHOR]

Traditional customized buses travel on fixed routes, which cannot satisfy passengers' flexibility and convenience requirements. This paper studies a demand-responsive transit (DRT) service that can continuously adjust the path based on passengers' dynamic demand. The path optimization model is established with more realistic constraints to create a bus travel plan within a specified area, and the model not only considers the preferred time windows of passengers but also maximizes the benefits of the system. Based on simulated annealing, a dynamic genetic algorithm is designed to generate the static initial travel path, and the dynamic travel path is continuously updated to satisfy the real-time demand. To evaluate the proposed model and algorithm, a case study in a typical residential community of Beijing, China, is conducted based on transit smart card records. According to the case study results, the convenience, travel time, and economic and environmental benefits of the DRT service are assessed via comparison with traditional buses and private cars. The analysis results demonstrate the feasibility and significance of the method, and it can be used by transit planners to design a superior DRT service. [ABSTRACT FROM AUTHOR]

Airport bus is an important public transportation mode for large international airport. To improve the bus station coverage, passenger demand compatibility and the scheduling flexibility of Beijing International Airport bus line, a dynamic line generation and vehicle scheduling method is proposed in this paper. Firstly, based on multi-source big data from the airport (including data from taxi, ride-hailing service, subway, regular bus, airport bus, etc.), we accurately extract candidate stations, which are very popular with passengers and convenient for parking and transfer, through public transportation demand level calculation, iterative clustering and POI matching. Then, the candidate stations need to be partitioned appropriately by selecting suitable features and calculating the similarity of candidate stations, so as to make the stations within each group a moderate size and have a consistent spatial orientation. Finally, a line generation and vehicle scheduling algorithm, which is compatible with multi-vehicle, high success rate of ride-sharing matching and low cost, is designed to realize accurate and rapid operation scheduling within each group according to the situation of passengers booking tickets. We have carried out experiments in Wangjing and Yayuncun, and the results show that our method can satisfy passenger demand fast and accurately. [ABSTRACT FROM AUTHOR]

In China, overlapped routes from multiple bus lines are widespread. Some buses are overcrowded while others have few passengers. The goal of this study is to reduce onboard passenger crowding without wasting bus capacity. This paper examines an extended multiple-depot vehicle scheduling problem (VSP) with multiple vehicle types. First, a bi-objective integer programming model was created. The trade-off between operating cost and passenger travel cost was established and the crowding cost due to different vehicle sizes was especially considered. Then, the capacity restraint incremental assignment algorithm and a vehicle scheduling method with multiple vehicle types were developed to find Pareto-optimal solutions that considered vehicle capacity constraints. Finally, the proposed methodology was validated using a real-world transit network in Beijing, China. The optimization results from about 1.18 million bus card swiping records show that our model and solution algorithm can effectively reduce the passenger crowding cost and avoid the waste of transport capacity. [ABSTRACT FROM AUTHOR]

• Construct a unified model for integrating timetable, bus formation and vehicle scheduling problems. • Schedule autonomous modular vehicles (AMVs) in both directions simultaneously. • Design a decomposition-based iterative solution framework based on Alternating Direction Method of Multipliers (ADMM) algorithm to solve the origin model efficiently. • Solve the subproblem efficiently with a customized time-dependent shortest path algorithm. • Introduce AMVs into public transport systems can reduce overall system costs and increase vehicle utilization. This paper presents a joint optimization of the timetable, bus formation, and vehicle scheduling in a flexible public transport (PT) system that utilizes autonomous modular vehicles (AMVs). In this system, AMVs have the capability to detach or join with each other at intermediate stops along the route to dynamically adjust the bus formation (capacity). To increase vehicle utilization, a flexible scheduling strategy is proposed that allows AMVs to detach from one modular bus and join another modular bus in either direction of a bidirectional line. In particular, the penalty cost for each detachment or joining operation, as well as the limited number of available AMVs is explicitly considered. We formulate a unified model for the integrated optimization of the modular bus service (timetable and bus formation) and vehicle scheduling by introducing two types of decision variables. The objective is to minimize overall system costs, including passenger waiting time cost, operational costs, and detachment/joining penalty costs. The two types of decision variables are coupled by a vehicle resource consistency constraint, ensuring the conformity of the modular bus service and vehicle scheduling decisions. To tackle the complexity of our model, the Alternating Direction Method of Multipliers (ADMM) is employed to decompose it into two subproblems, which can be efficiently solved using a customized forward dynamic programming algorithm and a commercial solver. The model is validated using illustrative examples and a real-world instance from the Beijing Public Transport system, and it is compared with two benchmark models. Our results demonstrate the efficiency of the ADMM-based solution framework for solving the integrated optimization model. Furthermore, our findings indicate that the use of AMVs in PT systems can lead to reduced overall system costs and increased vehicle utilization. [ABSTRACT FROM AUTHOR]

User profiles, considered as one of the fundamental inputs of recommendation systems and customized services, can be rationally applied in the public transport domain to represent passengers' characteristics and behavioral preferences. A user profile of a bus passenger, termed as a bus passenger profile (BPP), is an assortment of labels containing passengers' travel features. This paper proposes a data-driven framework for developing BPP so as to provide guidance on how to create and estimate user profiles for bus passengers based on smart card data. The proposed method comprises three steps. (i) Data preprocessing aimed at extracting key information and preparing passenger profiling. (ii) A tag system aimed at storing the estimated travel features of passengers. (iii) Knowledge graphs aimed at connecting various BPP with semantic edges for practical application of prior knowledge in downstream tasks. The developed framework is implemented in a case study of the Beijing bus system. Deployment of the developed framework has demonstrated that it can satisfactorily develop BPP, while prior knowledge from the BPP-based knowledge graphs can benefit downstream tasks. [ABSTRACT FROM AUTHOR]

In recent years, customized bus (CB) service has become an innovative model for public transportation in China. Its ability to provide advanced, timely, personalized, and flexible service has made it very popular. However, previous studies have ignored its diversion effect: How can CB service help conventional buses reduce on-board crowding during rush hours? In this paper, we optimize conventional and CB buses as a whole based on an empirical study in Beijing. We propose a new model to compare the cost of single conventional buses (Single Con) and the integration of conventional and customized buses (Con+ CB). Passenger diversions from conventional buses to CB ones are specifically considered. To do this we explicitly measure the on-board crowding cost of conventional buses by load factor and distance factor. Real-world data from Beijing, China, are employed to assess the model. We obtained the departure arrangements and the corresponding costs under different demand cases. The results are analyzed and compared. Sensitivity analyses are then conducted to study the effects of line-haul distance and load factor on cost. The results show that (1) adding new modes of transportation does not always significantly reduce the total cost of bus service; (2) CB buses can effectively ease congestion on conventional buses only at high demand densities but not at low densities; (3) crowding cost increases caused by the distance factor far exceed those caused by the load factor. • Explicitly measure crowding cost of conventional buses. • Propose cost models of Single Con buses and integration of Con+ CB buses. • Employ the real-world data in Beijing to assess the model. [ABSTRACT FROM AUTHOR]

Bicycle-sharing is an eco-friendly transportation operating model in the context of "Internet Plus" and the sharing economy. It not only meets the short-distance travel needs of residents but has great significance for promoting the sustainable development of urban public transportation. However, a series of problems have appeared in the bicycle-sharing market, such as unreasonable resource allocation, low operating efficiency and management difficulties. Based on booking data and GPS trajectory data in Beijing of Mobike, this paper statistically analyzes the spatial and temporal distribution characteristics of residents' bicycle-sharing trips. Then, we construct a multi-factor influence model of bicycle-sharing demand based on a negative binomial regression and variable selection model, which quantifies a series of factors that influence bicycle-sharing trips, such as population and the regional economy, building land attributes, transportation accessibility, weather, and climatic conditions, etc. The results show that, firstly, there is a spatial imbalance in the distribution of bicycle-sharing demand among different districts in Beijing. Bicycle-sharing demand is mainly concentrated in the six core districts of the city, with more than 80% of all demand. We also find that the bicycle-sharing demand has different distribution characteristics on working days and nonworking days. Compared with nonworking days, residents' demand for bicycle-sharing on weekdays shows obvious peak periods in the morning, noon, and evening. Secondly, factors that have a major impact on the demand for bicycle-sharing include: per capita disposable income, pass facilities, parking lots etc. Among them, factors such as per capita disposable income, pass facilities, parking lots and bus/subway stations have a significant positive influence on bicycle-sharing demand. However, the number of functional zones such as airports, ports and marinas, tourist attraction and automobile sales has a significant negative influence. In addition, a comfortable temperature and good air quality encourage residents to use bicycle-sharing more for travel, while high humidity is not conducive to bicycle-sharing. We suggest that companies and related departments should jointly participate in the regulation and management of the bicycle-sharing industry, in various aspects such as bicycle scheduling, bicycle management and industry systems. In this way, cities can allocate bicycle-sharing resources reasonably and improve overall operating efficiency. The advantages of bicycle-sharing can be better used to promote the sustainable development of urban public transportation in the future. • The temporal distribution of bicycle-sharing demand is imbalanced, including between working and nonworking days, and between peak and off-peak periods of a day. • The spatial distribution of bicycle-sharing demand is imbalanced across the various administrative districts and functional zones. • Factors such as income and pass facilities have positive influence on bicycle-sharing demand, but the functional zones such as airports and ports have negative influence. • The use of bike-sharing is sensitive to the weather and air quality. [ABSTRACT FROM AUTHOR]

Many cities around the world face the challenge of an aging population. A full understanding of the mobility behavior characteristics of the elderly is one necessary and urgent consideration as regards the current aging trend if sustainable urban development is to be fully realized. This paper presents a systematic approach to analyzing the dynamic mobility characteristics of the elderly who travel by bus using smart card big data. The characteristics include temporal distribution, travel distance, travel duration, travel frequency, and also the spatial distribution of such travelers. The findings of these mobility characteristics can directly contribute to both public transport policy making, service, and management. In this study, the analytics of the elderly are also compared with that of the average adult group so as to identify both the similarities and differences between the two groups. Beijing, a megacity, with a very high life expectancy and in which the bus is the dominant mode of public transport for the elderly, was used as the study area. The significance of this research concerns a newly developed systematic approach that is able to analyze the dynamic mobility characteristics of the elderly using smart card data. [ABSTRACT FROM AUTHOR]

As a product of the development of the Internet and the sharing economy, shared bicycles are beneficial in solving the last mile problem of public transportation for urban residents and expanding the service area of urban public transportation to a certain extent. This paper analyses the spatial-temporal characteristics of shared bicycles in the city of Beijing by using kernel density estimation and statistical analysis methods. The maximum coverage location problem model is used to quantify the effects of shared bicycles on the subway and bus urban public transportation systems. The analysis results are of great importance to identify the effects of shared bicycle usage on an urban bus public transportation system from the view of space-time. The results show that (1) shared bicycles are largely located within 1.5 km from the subway station and 500 m from the bus stations; (2) the number of the shared bicycle usage changes along with the distance from the subway and bus stations; (3) subway and bus stations are divided into six types based on the sink-source characteristics of shared bicycles; (4) the service areas of subway and bus stations are expanded by approximately 2.34 and 1.33 times, respectively, after the implementation of shared bicycles. [ABSTRACT FROM AUTHOR]