Congestion pattern in a busy traffic zone would not always be a single state. The scenario of congestion occurring in multiple connected road links may be shifted to the scenario of whole zone network paralyzation immediately. It is necessary to dynamically predict the congestion pattern for a busy traffic zone rather than a road link, which could provide information for network system decision making. Considering the close connection between the upstream and downstream traffic flow, this study proposes a congestion pattern prediction model for a busy traffic zone based on the hidden Markov model (HMM). The model establishes a correlation between the external road traffic state (observation state) and internal road traffic state (hidden state) of a busy traffic zone. We acquire these traffic states by cleaning and mining floating vehicle trajectory data. With these data, we calibrate the HMM and predict the zone congestion pattern. This article demonstrates the validity and rationality of the model by taking a hospital area in Ningbo City as an example. The prediction accuracy can reach 83.4%, which is 5.8% higher than that of the autoregressive moving average model. This case result illustrates the feasibility and effectiveness of our approach in the field of congestion pattern prediction for busy traffic zone.

The choice of investment strategy has a great impact on the performance of transport infrastructure. Positive projects such as the “Subway plus Property” model in Hong Kong have created sustainable financial profits for the public transport projects. Owing to a series of public debt and other constraints, public–private partnership (PPP) was introduced as an innovative investment model to address this issue and help develop transport infrastructure. Yet, few studies provide a deeper understanding of relationships between PPP strategy and the performance of such transport projects (particularly the whole transport system). This paper defines the research scope as a regional network of freeway. With a popular PPP model, travel demand prediction method, and relevant parameters as input, agents in a simulation framework can simulate the choice of PPP freeway over time. The simulation framework can be used to analyze the relationship between the PPP strategy and performance of the regional freeway network. This study uses the Freeway Network of Yangtze River Delta (FN-YRD) in China as the context. The results demonstrate the value of using simulation models of complex transportation systems to help decision makers choose the right PPP projects. Such a tool is viewed as particularly important given the ongoing transformation of functions of the Chinese transportation sector, including franchise rights of transport projects, and freeway charging mechanism.

Theoretically speaking, the data of a stated preference survey could be suggested for the calibration of a stochastic route choice model. However, it is unrealistic to implement the questionnaire survey for such a large number of alternative routes. Engineers generally determine the parameter empirically. This experienced choice of perception parameter may cause higher errors in the route flows. In our calibration model of the perception parameter, the data of the cellular network is set as the input. This model consists of two levels. The upper level is to minimize the gap squares of the route choice ratio between the C-logit model and the cellular network data. The stochastic user equilibrium (SUE) in terms of the C-logit model is used as the lower level. The simulated annealing (SA) algorithm is used to solve the model, where the route-based gradient projection (GP) algorithm is used to solve the inner SUE. A case study is used to validate the convergence of the model calibration. A real-world road network is used to demonstrate the objective advantage of an equilibrium constraint over a nonequilibrium constraint and explain the feasibility of the candidate routes assumption.

In order to improve the availability of taxi GPS big data, we restore the chosen route for the sparse taxi GPS trajectory in this work. A trajectory restoration method based on Bayesian network is proposed. Compared with the traditional...

This paper explores the factors influencing taxi travel in the context of COVID-19 from both demand and supply sides and provides a quantitative comparison of taxi travel characteristics and taxi industry operations before and after the epidemic. A model was established using system dynamics to simulate a taxi travel system, which was used to analyze the changes in demand and supply of taxi travel under scenarios such as closedowns, travel restrictions, etc. The analysis is based on a typical middle-sized city in China, Ningbo in Zhejiang Province, revealing factors leading to the significant drop in the amount of taxi travel due to the epidemic. The study can provide insights into impacts of public (or similar anomalous or catastrophic) events on taxi travel systems and could be useful for urban transport planning and management.