Mobile phone GPS data in urban customized bus: Dynamic line design and emission reduction potentials analysis

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.

A Multi-Objective Robust Optimization Model for Customized Bus Routes

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.

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Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

This study investigates flexible bus that provides door-to-door service with multiple passengers sharing the vehicle, which reduces congestion on the urban network. The service area is divided into zones, and flexible buses are assigned to zonal routes based on the historical demand characteristics before demand realization. Passengers are served by either regular service or ad hoc service after the demand realization to minimize the sum of ad hoc service cost and detour time cost. The elastic and stochastic natures of demand are captured in the formulation by the volume stochasticity of demand, detour time stochasticity, and elasticity of demand with respect to the flexible bus service price and quality. The profit of the flexible bus service is maximized while accounting for the detour time cost. To effectively solve the problem, volume and detour time reliability measures are introduced to separate the problem into a vehicle-to-route assignment problem and a passenger-to-vehicle assignment problem. A gradient-based solution approach is devised to determine the flexible bus routing plans by optimizing the associated reliability measures. The solution approach is further improved by combining the gradient-based approach with a greedy search solution approach and relaxing the formulations. The formulation and solution approaches are implemented using real data in Chengdu, China, with promising results.

Zonal-based flexible bus service under elastic stochastic demand

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.

https://www.mdpi.com/2071-1050/12/15/6203/pdf

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

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.

Hui Li

Papers

Customized Bus Network Design Based on Individual Reservation Demands