Integrated route choice and assignment model for fixed and flexible public transport systems

Abstract

The recent technological innovations in various ICT platforms have given rise to innovative mobility solutions. Such systems could potentially address some of the inherent shortcomings of a line/schedule based public transport system. Previous studies either assumed that flexible ondemand services are used as an exclusive door-to-door service or offered as a feeder connection to high-capacity public transport services. However, users may combine line/schedule based public transport systems (Fixed PT) and on-demand services (Flexible PT) so that their travel impedance is minimized. To this end, we propose a multimodal route choice and assignment model that allows users combining Fixed and Flexible PT or use them as individual modes while demand for these services is endogenously determined. The model takes into account the dynamic demand-supply interaction using an iterative learning framework. Flexible public transport can be used to perform any part of the trip, ranging from a first/last mile service to an exclusive direct door-to-door connection. The developed model is implemented in an agent based simulation framework. The model is applied to a network centered around the city of Amsterdam, The Netherlands. Scenarios where Fixed PT and Flexible PT are offered as mutually exclusive modes or can be combined into a single journey, are analysed. Results indicate that Flexible PT is predominantly used for covering <30% of the trip length, indicating that it is mainly used as an access or egress mode to Fixed PT. This results with an overall increase in the share of public transport trips. Also, the average waiting time of Flexible PT users when used in combination with Fixed PT are lower than the scenario where each of them is used as an exclusive mode.

A Literature Review on Interactions Between Stakeholders Through Accessibility Indicators Under Mobility as a Service Context

“…The platform side focused on matching both users and vehicles, route planning [38][39][40][41][42][43][44][45][46][47][48][49], and the impact of MPO on users [50][51][52]. The modeling part focused on modeling the demand-supply interactions [48,[53][54][55][56][57][58][59][60][61][62][63][64][65][66][67].…”

“…From a modeling perspective, Narayan et al developed an agent-based model (ABM) considering total travel time (walking time, waiting time, and in-vehicle travel time), fare, travel distance, and number of transfers to model multimodal route choices; however, the model could not consider the influence of user preferences on mobility options [60]. In their study, the user demands were adjusted by changes in travel time, waiting time, fare policies, and operational plans through iterations implemented in the ABM.…”

Route Configuration Method for Highway Passenger Hubs from the Perspective of Transportation Integration: A Case Study of Nanjing, China

“…Many studies have been conducted to the optimise passenger routes and networks; four passenger transport types have been investigated in these studies: integrated, urban, suburban, and a type related to electric vehicles and big data. The route configuration models proposed in comprehensive transportation network design research works are based on the minimum cost theory, the large-scale city line allocation method, and the micro-optimisation method [9][10][11][12]. Abramović et al [9] presented a possible solution for integrated passenger transport organisation on the Zagreb-Velika Gorica route, with the emphasis on connecting the Velika Gorica railway station to the town of Velika Gorica.…”

“…A simple but sparse network of direct and frequent services was designed in Melbourne, Australia [10]. Narayan et al [11] proposed a multimodal route choice and assignment model that allows users to combine fixed and flexible public transport or to use them as individual modes, while the demand for these services is endogenously determined.…”

“…, n) is the relative generalised travel cost from zone m + 1 to passenger hub j. Thus, the distribution problem of the line is equivalent to the model presented in Equation (11). The on-table method is used to solve the model; the flow of the algorithm is shown in Figure 3.…”