The Integration of Multimodal Networks: The Generalized Modal Split and Collaborative Optimization of Transportation Hubs

Abstract

Integrated transportation is one of the most important methods to encourage the modal shift from car to public transportation (PT). However, as most cities have an existing multimodal network, it is difficult to expand the current networks by building more PT routes. Thus, integrating different modes through the optimization of hubs is a cost-efficient way to promote sustainable mobility. This paper develops a bilevel multimodal network design problem based on the collaborative optimization of urban transportation hubs. The upper-level problem is formulated as a mixed-integer nonlinear program to achieve a modal shift from congested subnetworks to underutilized subnetworks to realize a balanced use of the entire network. The decision variables are classified into location-based (hub locations) and route-based (route layouts and frequency setting) ones. The lower-level problem is a generalized modal split/traffic assignment problem (GMS/TAP), which captures the mode choices of all modes in the path set. The GMS/TAP is formulated as a nonlinear optimization problem (NLP) and is solved using a hybrid method of the successive average (MSA) algorithm. A hybrid genetic search with advanced diversity control (HGSADC) is developed to solve the bilevel model, where the exploration of the search space is expanded using the biased fitness function and diversification mechanism. The solution properties of the hybrid MSA and HGSADC are demonstrated in two modified nine-node networks. The model performance is illustrated in a real-size network in Jianye district, Nanjing. 9.2% decrease of travel time, 25.7% increase of service level, and a significant modal shift from car to PT are obtained.

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

“…From the Asian applications, China was the country with more studies (comprising 48 articles [38,40,41,46,), from which Beijing stands out with 17 studies [62, 63, 65, 74, 80, 81, 83-85, 87, 88, 92-94, 96, 98, 102]. Real networks or case study applications may include universities' campi [36,[103][104][105][106][107][108], neighbourhoods and districts [46,80,95,98,102,109], or studies that only consider some transport lines/stations within a city or transport network [71,81,86,93,96,[110][111][112].…”

“…There were eight studies that applied traffic management techniques and optimisation. The transport modes included cars [46,58,60,136] and multimodal networks of bicycles, buses, cars, rail, and shared mobility systems [95], of buses and cars [157], and of buses, rail systems, and cars [125]. The decision variables included network design (corridors and/or stations' location, routes) [58,64,95,125], network modifications (to reverse road traffic flow or to leave it unaltered) [136], frequencies setting [95,125,197], fleet sizing [197], policy variables [58,125,157], modal split [64], and traffic management [46,60].…”

“…The transport modes included cars [46,58,60,136] and multimodal networks of bicycles, buses, cars, rail, and shared mobility systems [95], of buses and cars [157], and of buses, rail systems, and cars [125]. The decision variables included network design (corridors and/or stations' location, routes) [58,64,95,125], network modifications (to reverse road traffic flow or to leave it unaltered) [136], frequencies setting [95,125,197], fleet sizing [197], policy variables [58,125,157], modal split [64], and traffic management [46,60]. For decisions on policy variables, Dantsuji et al [157] sought to determine the level of congestion pricing for the cars and the road space required for both two modes (buses and cars), e.g., using mixed or dedicated lanes, Li et al [58] aimed to design a toll model, and Amirgholy et al [125] analysed different scenarios with mixed network (bus), dedicated lanes (BRT) and parallel network (metro).…”

“…Objective functions were related to minimise traffic density/volume [46,136,197], travel times [197], congestion cost [157], total system travel disutility and each road user's travel disutility [58], passengers', operators', and external costs [125], total passengers' travel times, construction costs, modal shift value, and balanced use value [95], travel delay, stops, fuel consumption, and integrated performance index (including CO, HC and NO x emissions) [60], total vehicles' emissions cost [136], and to maximise traffic flow [46]. At last, as the decision variable of Feng et al [64] was whether to implement a policy instrument (bus fare, additional private car toll, bus lane construction, and large-scale bus fleet purchase) to change the current modal split into a more sustainable one, the objective function aimed to minimise the difference between the actual and desired proportion of each mode after a combination of traffic demand management policy instruments.…”

Assessing the Operation of a Multimodal Hub: A Traffic Impact Microsimulation Analysis

“…In this way, new users will be attracted and the commitment between the commuters and the local transportation system will be strengthened. As most cities have an existing multimodal network, it can be time-and cost-consuming for governments to create changes in current networks [7], but undoubtedly all these changes contribute to healthier, sustainable living in cities [8].…”