In the MaaS (Mobility as a Service) age, the alternatives of route choice for a trip will not be the single mode paths, but the combined routes utilizing more than one travel mode in the multi-modal transportation systems. When modeling the multi-modal combined route choices, alternative routes are correlated not only because of the overlapping of physical links, but also because of the overlapping of travel modes. We define this problem as the generalized path overlapping problem. To address the generalized overlapping problem, a multi-modal logit kernel (MLK) model is proposed to explicitly consider the correlations of unobserved utilities of combined routes. In this model, the unobserved utilities of combined routes are divided to two parts, the link specific parts and the independent route specific parts. The link specific parts are further divided to two parts: physical link specific and the mode specific. The generalized path overlapping problem is captured by the sharing of these two parts of unobserved link utilities. Based on this model, the stochastic user equilibrium on multi-model transport networks is represented as a fixed-point problem. Numerical studies are designed to illustrate the effects of incorporating generalized overlapping problem on disaggregated route choice prediction and aggregated traffic flow assignment. With different settings of physical link and mode specific error terms, the variations of disaggregated route choice predictions and aggregated traffic flows are discussed. It is found that, the generalized overlapping problem can be captured by the proposed model and significantly affect both the individual route choice predictions and aggregated traffic flows at equilibrium.

In highway on-ramp sections, the conflictual interactions between a subject vehicle (merging vehicle) in the acceleration lane and a following vehicle (lagging vehicle) in the adjacent mainline can lead to traffic congestion, go–stop oscillations, and serious safety hazards. Human drivers combine their previous lane-changing experience and their perception of surrounding traffic conditions to decide whether to merge. However, the decisions that they make are not always optimal in specific traffic scenarios due to fuzzy perception and misjudgment. That is, they make lane-changing decisions in a bounded rational way. In this paper, a game-theory-based approach is used to model the interactive behavior of mandatory lane-changing in a highway on-ramp section. The model comprehensively considers vehicle interactions and the bounded rationality of drivers by modeling lane-changing behavior on on-ramps as a two-person non-zero-sum non-cooperative game with incomplete information. In addition, the Logit QRE is used to explain the bounded rationality of drivers. In order to estimate the parameters, a bi-level programming framework is built. Vehicle trajectory data from NGSIM and an unmanned aerial vehicle survey were used for model calibration and validation. The validation results were rigorously evaluated by using various performance indicators, such as the mean absolute error, root mean square error, detection rate, and false-alarm rate. It can be seen that the proposed game theory-based model was able to effectively predict merging and yielding interactions with a high degree of accuracy.

Generally, low-mobility individuals, those with less access to transportation options and who are often marginalized in the social environment, can be divided into three groups: older adults, low-income individuals, and individuals with disabilities. This article analyzes the transportation demands of low-mobility individuals in China using travel characteristics and accessibility of public transportation. A mixed survey procedure, including a large-scale investigation and a supplementary investigation was used. The findings show that (1) there are different results regarding the transportation demands of the three low-mobility groups; (2) as a single entity, low-mobility individuals have similar transportation demands, which are inferior to those of the general public; and (3) the transportation demands of low-mobility individuals in China are not identical to those obtained by other researchers. The above results indicate that the analysis of the transportation demands of low-mobility individuals should not only consider the transportation demands of distinct groups of low-mobility individuals but should also consider all of them as a single entity in comparison to the general public. Furthermore, planners in developing countries who make policies for the improvement of transportation conditions for low-mobility individuals should gain advanced experience from developed countries and take the particular conditions of their own country into account in order to meet the demands of low-mobility individuals.

Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers.

Various factors can make predicting bus passenger demand uncertain. In this study, a bilevel programming model for optimizing bus frequencies based on uncertain bus passenger demand is formulated. There are two terms constituting the upper-level objective. The first is transit network cost, consisting of the passengers’ expected travel time and operating costs, and the second is transit network robustness performance, indicated by the variance in passenger travel time. The second term reflects the risk aversion of decision maker, and it can make the most uncertain demand be met by the bus operation with the optimal transit frequency. With transit link’s proportional flow eigenvalues (mean and covariance) obtained from the lower-level model, the upper-level objective is formulated by the analytical method. In the lower-level model, the above two eigenvalues are calculated by analyzing the propagation of mean transit trips and their variation in the optimal strategy transit assignment process. The genetic algorithm (GA) used to solve the model is tested in an example network. Finally, the model is applied to determining optimal bus frequencies in the city of Liupanshui, China. The total cost of the transit system in Liupanshui can be reduced by about 6% via this method.