Customized Bus Network Design Based on Individual Reservation Demands

Abstract

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 c0C. The CB vehicle use decreases as c0C ccincreases. The results of this study can provide technical support for CB operators who design CB networks.

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

“… Li et al (2019) analyzed the operational conditions of CB in Shanghai from the aspects of service level, in-vehicle time, travel time, and ticket price. Focusing on the people-bus-route matching problem, Han et al (2019) discovered the combination of 49-seat CB and 18-seat CB was the most economical of all options. Besides, CB with lower capacity tended to be more cost-effective than that with higher capacity.…”

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

“…Lyu et al [32] developed a framework called CB-Planner to jointly optimize stop locations, bus routing and timetables, and probabilistic mode choice, which maximized total profit. Han et al [33] presented an analytical model that minimized total cost and demonstrated that a mixed-size of bus fleet is cost-effective than a single-sized bus fleet. According to the findings discussed above, Table I summarizes the features of key studies focusing on the CB service.…”

An Analytical Model for the Many-to-One Demand Responsive Transit Systems

“…That is because the vehicle with a larger capacity would pick up more passengers in a trip. However, according to Equation (6), the increase in occupancy increases the system's average service rate, which decreases the unit time of serving a passenger. Hence, passengers could make trip requests close to their desired time to decrease their travel costs.…”

“…Thanks to the advanced information and telecommunication technologies, travelers are allowed to take on-demand services or subscription bus services, especially in remote areas during peak hours [4,6]. An apparent trade-off is that transit users have to wait for the vehicle to pick them up and then stay in the vehicle, which may detour to pick up and drop off other users.…”