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Accurate prediction of bus arrival time is essential to achieve efficient bus dispatch and improve bus trip sharing rate. This article proposes using the improved whale optimization algorithm-long short-term memory (IWOA-LSTM) model to predict bus arrival times and improving the whale algorithm by optimizing the hyperparameters of the LSTM model, so that the advantages and disadvantages of the whale algorithm and the LSTM model can complement each other, thus enhancing the robustness of the model. Initially, the bus arrival process and its associated influencing factors are analyzed, with certain factors being quantified to serve as input features for the prediction model. After processing the GPS data of the No. 220 bus in Nanchang, Jiangxi, China, the proposed prediction model is analyzed and validated using an example and compared with other prediction models. The results show that the IWOA-LSTM prediction model has the best-fitting effect between the predicted values and actual values in all time periods. Its MAPE, RMSE, and MAE have been reduced by at least 9.47%, 12.77%, and 8.93%, respectively, and the overall R[sup.2] has been improved by at least 10.65%. These results indicate that the model has the best predictive performance., Author(s): Bing Zhang (corresponding author) [1]; Lingfeng Tang [2]; Dandan Zhou [3]; Kexin Liu [4]; Yunqiang Xue [4] 1. Introduction The continuous increase in the number of motorized vehicles has [...]

The primary objective of this paper is to minimize the overall travel costs for passengers while simultaneously maximizing the operational revenue for the transportation company. This is achieved through the optimization and adjustment of various factors, such as the intervals between regular bus and subway services, the duration of vehicle stops at each station, and the pricing structure for subway and shared bicycle usage. By enhancing the efficiency of passenger travel, we have successfully bolstered the company's operational profits. In contrast to prior research, this paper comprehensively considers the dual uncertainties associated with both bus operations and shared bicycle operations within a cooperative system. By establishing a coordinated dual-level optimization model for regular bus, subway, and bike-sharing networks under dual uncertainty conditions, we employed convex combination techniques to unify the dual uncertain variables into a single objective, which was then incorporated into a chance-constrained bilevel programming model. Ultimately, we utilized KKT conditions to transform the model from a bilevel to a single level for resolution. This paper centers its research on the collaborative system comprising the Nanchang Metro Line 1, Bus Route 520, Bus Route 211, and the adjacent region hosting a cluster of shared bicycles. By leveraging Python programming, optimization models, empirical data on traffic flow and stoppage times, and OD data, we conducted an optimization analysis to solve the problem at hand. According to the optimization results, passenger waiting time, passenger transfer time, and passenger on board time are effectively reduced by 6.81%, 18.29%, and 23.92%. At a confidence level of 95%, the resulting time level results in a 12.44% reduction in total travel time. The average subway fare increased by 18.12%, the average shared bicycle fare decreased by 19.12%, and the total cost of travel expenses increased by 16.68%. The final total cost of travel was reduced by 4.06%, and the business operating income was increased by 13.10%. The comprehensive optimization results have effectively fulfilled the objectives of the bilevel optimization model, thereby confirming the rationality and practicality of the optimization approach. The research outcomes hold significant practical implications for facilitating the efficient and cooperative development of urban transportation networks, ultimately enhancing the convenience of residents' travel experiences., Author(s): Yunqiang Xue (corresponding author) [1,2]; Tong He [1]; Tao Li [3]; Hongzhi Guan [4]; Yang Qiu [1] 1. Introduction With the acceleration of urbanization, the transportation demands in large [...]

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap. [ABSTRACT FROM AUTHOR]

In the optimization process of the routes of customized buses, there are numerous uncertainties in the route planning and setting. In this study, the uncertainty theory is introduced into the optimization problem of a customized bus route, and an uncertain customized bus route optimization model is established, which aims at the minimizing the total mileage of vehicle operation. An improved genetic algorithm is used to solve the model, whose feasibility is verified by a case study. The results show that the optimization model based on the uncertainty theory can yield a reasonable customized bus route optimization scheme, and the total mileage reduced from 35.6 kilometers to 32.2 kilometers. This research provides the theoretical support for the optimization of customized bus routes., Author(s): Zi Sang [1]; Bing Zhang (corresponding author) [1]; Yunqiang Xue [1]; Hongzhi Guan [2] 1. Introduction Public transport priority is an important approach to alleviate traffic congestion. Customized public [...]

This paper investigates the latent classes of parking preference for drivers and the economic effects after implementing Parking Space Comprehensive Utilization (PSC) in traditional business districts (TBD), with a particular focus on the parking preferences of electric vehicle users (EVU). Firstly, Exploratory Factor Analysis (EFA) is used to reduce dimensionality and determine the latent structure. Then, based on the Latent Class Model (LCM), the customers are classified, and the proportion of each class under various latent variables is analyzed. Finally, the paper conducts a quantitative analysis of economic effects by considering different psychological factors across different customer classes. With the data obtained from revealed preference (RP) and stated preference (SP) surveys, this paper identifies the customers' preferences for the three scenarios presented. The results show that (1) customers can be classified into four classes: core customers (CCS, 34%), potential customers (PCS, 29%), regular customers (RCS, 22%), and marginal customers (MCS, 15%), among which EVU do not show a significant preference for parking charging facilities in TBD; (2) the potential economic improvements for these four classes are: 9%, 12%, 8%, and 10%; (3) CCS has the greatest potential to increase store revenue by ¥7041, while PCS has the greatest potential to increase store customer flow by 31%. These findings provide a valuable reference for decision-making by TBD store managers. [ABSTRACT FROM AUTHOR]

For travelers who inevitably use motor vehicles, in the case of limited parking spaces, reserving parking spaces in destination in advance helps reduce the time and emissions of searching for parking spaces and alleviate road traffic pressure. From the perspective of bounded rationality, this paper comprehensively considers the impact of traveler's personal attributes and behavioral characteristics on parking reservations. The data processing analysis shows that the traveler's age, gender, monthly income, and other characteristics have a certain impact on the parking reservation choice behavior. Reservation price is the key factor affecting the parking reservation policy. Travelers show different value perceptions of the reserved price of parking spaces, and this process has been verified to be roughly the same as the prospect theoretical model. As the reference point for highest reservation price becomes larger, travelers tend to choose to pay less than the ideal reservation price and become more sensitive to losses. It can be found from the model functions and survey data that the ideal reserved parking space price in the survey area is 5 yuan per hour which equals the normal parking fee, and the ideal parking reservation time is less than 2 hours. The research results provide a basis for formulating reasonable parking reservation schemes and parking policies., Author(s): Yunqiang Xue (corresponding author) [1,2]; Lin Cheng (corresponding author) [1]; Ping Lin [3]; Jing An [2]; Hongzhi Guan [2,4] 1. Introduction With the development of China's economy and society, [...]

Based on uncertainty theory, this paper proposed a vehicle scheduling method considering the dynamic departure interval and vehicle configuration of electric buses (EBs). An uncertain bi-level programming model (UBPM) is established, which takes the total cost of passenger travel (CP) as the upper and total cost of EBs (CB) as the lower. A chance constrained programming model (CCPM) based on the randomness of passenger waiting time and the uncertainty of interstation running time is proposed as the upper model. With a certain confidence level of service level as constraints, the goal is to minimize the total cost of passenger travel. Then, an expected value model (EVM) based on the fluctuation of energy consumption is proposed as the lower model. Taking the number of EBs as the constraint condition, the goal was to minimize the energy consumption of EBs. Finally, a practical bus route is taken as an example to verify the effectiveness of the proposed method. The results demonstrated that the optimal scheduling plan considering the uncertain variables can reduce the passenger travel cost. Collaborative optimization of EBs vehicle configuration can reduce energy consumption, delay, and the number of EBs. [ABSTRACT FROM AUTHOR]

In order to better understand how commuters decide departure time considering parking space shortage and commuters' bounded rationality, the reference point hypothesis of prospect theory is applied in the departure time decision-making. Commuter personal perception differences, the road congestion situation, destination parking status, and other factors were also analysed in the influence of commuter departure time choice. Based on prospect theory, an experiment was designed to investigate the intention of the commuter departure time choice. The experiment results show that the commuter's travel satisfaction and the departure time choice of the next trip are related to the parking space residual status after the commuter arrives at the destination. The satisfaction degree of the commuter is reduced, with the decrease of the remaining parking spaces. If the commuter is satisfied with the travel result, the commuter's departure time of next trip tends to be later. In the case of illegal parking, different penalty measures may lead to different decisions of next departure time choice. A commuter tends to depart earlier when more severe punishment for illegal parking is enforced. The research results can reveal to some degree the travellers' departure time choice behaviour when they face the risk of no parking spaces and provide a theoretical and practical support for parking management and car travelling decision., 1. Introduction Along with economic development and urbanization, the amount of private cars in China has rapidly increased, which leads to hard parking, traffic congestion, emissions, and other transportation problems [...]

In the optimization process of the routes of customized buses, there are numerous uncertainties in the route planning and setting. In this study, the uncertainty theory is introduced into the optimization problem of a customized bus route, and an uncertain customized bus route optimization model is established, which aims at the minimizing the total mileage of vehicle operation. An improved genetic algorithm is used to solve the model, whose feasibility is verified by a case study. The results show that the optimization model based on the uncertainty theory can yield a reasonable customized bus route optimization scheme, and the total mileage reduced from 35.6 kilometers to 32.2 kilometers. This research provides the theoretical support for the optimization of customized bus routes. [ABSTRACT FROM AUTHOR]