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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.

Determining the required probe vehicle size for real-time travel time estimation is an important issue in probe-data-based applications, such as traffic monitoring on a roadway network. This paper provides an innovative approach for the determination of proper sample sizes with the aid of a simulation model. The model, which was built for El Camino Real, a major arterial in the California San Francisco Bay Area, helps to provide the necessary detailed data to verify the proposed methodology. Our proposed approach is based on the findings that the distribution of travel time estimate error can be constructed with the generalized Pareto distribution function. Considering that travel times vary within a signal cycle and among different movements (through, left-turning and right-turning) at intersections, our study uses data in 15-s intervals to examine the travel time variations associated with different intersection movements. Our findings show that when the penetration rate of probe vehicles is 40%, the probability that the relative error will be less than 0.10 within the time period is 90%, whereas a penetration rate greater than 60% is needed to attain a 0.95 confidence level at the same error rate. For left-turning vehicles, a 10% penetration of probe vehicles is sufficient for estimating their travel times with relative errors less than 0.10 at a 90% confidence level, whereas through vehicles and right-turning vehicles require substantially higher penetration rates of approximately 50% and 30%, respectively, to attain the same confidence level.

Non-coordinated first-train timetables can result in unfavorable train connections among different subway lines at transfer stations and generate rather long waiting time for transfer passengers. This paper aims at optimizing the first train originating times of different transit lines at the network scale. The cost function of transfer waiting time is formulated to evaluate the perceived transfer quality with the consideration of passengers' psychological feelings. The first-train timetable coordination model is then developed by minimizing the total waiting cost of passengers transferring between two first trains of different lines. The genetic algorithm is applied to solve the model. Finally, a case study from part of the Beijing subway network is conducted to verify the method. The results show that the total waiting cost of first-train transfer passengers is reduced by 49.67% with the application of our proposed model. Meanwhile, the number of super-long waiting is significantly reduced.