A Frequency-Based Assignment Model under Day-to-Day Information Evolution of Oversaturated Conditions on a Feeder Bus Service

Abstract

Day-to-day information is increasingly being implemented in transit networks worldwide. Feeder bus service (FBS) plays a vital role in a public transit network by providing feeder access to hubs and rails. As a feeder service, a space-time path for frequent passengers is decided by its dynamic strategy procedure, in which a day-to-day information self-learning mechanism is identified and analyzed from our survey data. We formulate a frequency-based assignment model considering day-to-day evolution under oversaturated conditions, which takes into account the residual capacity of bus and the comfort feelings of sitting or standing. The core of our proposed model is to allocate the passengers on each segment belonging to their own paths according to multi-utilities transformed from the time values and parametric demands, such as frequency, bus capacity, seat comfort, and stop layout. The assignment method, albeit general, allows us to formulate an equivalent optimization problem in terms of interaction between the FBS' operation and frequent passengers' rational behaviors. Finally, a real application case is generated to test the ability of the modeling framework capturing the theoretical consequents, serving the passengers' dynamic externalities.

Evaluating the Quality of Service of Bus Performance in Johor Bahru

“…The concession was handled to specific operators because they can control the QOS [4]. Globalisation and technology had led towards industrial revolution (IR) 4.0, in which every angle of the daily life is connected with Internet of Things (IOT) [34]. The government should assist key players in bus industries to develop and implement a new technology in bus services to improve its quality of for bus performance, such as actual time bus services [34], designing a significant feeder transit [32], optimisation of bus schedules [31], and systematic departure and arrival of buses [28].…”

“…Globalisation and technology had led towards industrial revolution (IR) 4.0, in which every angle of the daily life is connected with Internet of Things (IOT) [34]. The government should assist key players in bus industries to develop and implement a new technology in bus services to improve its quality of for bus performance, such as actual time bus services [34], designing a significant feeder transit [32], optimisation of bus schedules [31], and systematic departure and arrival of buses [28]. By adapting this technology, trustworthiness and passenger and user perceptions towards bus services can also be increased as operators can deliver services of good quality [24].…”

Hybrid Random Regret Minimization and Random Utility Maximization in the Context of Schedule-Based Urban Rail Transit Assignment

“…They introduced the probability of "fail-to-sit" at boarding points to calculate the travel cost and distribute the passenger flow. Zhang et al proposed an assignment model based on frequency considering day-today evolution under oversaturated conditions and studied the impact of passenger comfort on the overload conditions and frequency of departure on passenger route selection [13]. Leurent et al considered the capacity and provided a static and macroscopic traffic assignment model from the line submodel and the network [14].…”

“…The RRM model develops from the angle of bounded rationality and captures the scheme between multiple attribute tradesoffs to the traveler's choice of psychological and traffic behavior based on minimization of the perceived regret decision criteria [3]. Recently, regret-based choice models have gained in popularity in travel behavior research, as an alternative approach to modeling choice behavior, under conditions of both certainty and uncertainty [32,37,38] [14] bus transit network RUM × √ × Zhang S et al [13] feeder bus service RUM × √ × Tong et al (1998) urban rail transit RUM √ × √ Poon et al [23] bus transit network RUM √ √ × Hamdouch et al [22] bus transit system RUM √ √ × Nuzzolo et al [28] bus transit system RUM √ √ × Han B et al [29] urban rail transit RUM √ √ √ Chorus et al [32,37] health-related choices, and policy choices [39,40]. Chorus shows that RRM can be extended to the case of risky travel choice [32,37].…”