Academic literature on the topic 'Travel time (Traffic estimation)'

Vehicles travelling on urban streets are heavily influenced by traffic signal controls, pedestrian crossings, and conflicting traffic from cross streets, which would result in bimodal travel time distributions, with one mode corresponding to travels without delays and the other travels with delays. A hierarchical Bayesian bimodal travel time model is proposed to capture the interrupted nature of urban traffic flows. The travel time distributions obtained from the proposed model are then considered to analyze traffic operations and estimate travel time distribution in real time. The advantage of the proposed bimodal model is demonstrated using empirical data, and the results are encouraging.

Providing reliable real-time travel time information is a critical challenge to all existing traffic routing systems. This study develops a new model for estimating and predicting real-time traffic conditions and travel times for variable message signs-based route guidance system. The proposed model is based on real-time limited detected traffic data, stochastic nonlinear macroscopic traffic flow model, and adaptive Kalman filtering theory. The method has the following main features: (1) real-time estimation and prediction of traffic conditions on a network level using limited traffic detectors, (2) travel time prediction in free flow and congested flow, and (3) prediction of drivers’ en-route diversion behavior. Field testing is conducted based on the Route Guidance Pilot Project sponsored by the National Science and Technology Ministry of China. The achieved testing results are satisfactory and have potential use for future works and field applications.

With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link travel-time forecasting, and (iv) corridor / route travel-time forecasting. This study applied cross validated mean square error (CVMSE) model for the link and route travel-time estimations, and a forecasting mean square error (FMSE) model for the link and corridor / route travel-time forecasting. These models were applied to loop detector data obtained from the Kyeongbu expressway in Korea. It was found that the optimal aggregation sizes for the travel-time estimation and forecasting were 3 to 5 min and 10 to 20 min, respectively.

In this paper, the calculation method of the link travel time is firstly analysed in the continuous traffic flow by using the detection data collected when vehicles pass through urban links, and a theoretical derivation formula for estimating link travel time is proposed by considering the typical vehicle travel time and the time headway deviation upstream and downstream of the links as the main parameters. A typical vehicle analysis method based on link travel time similarity is proposed, and the theoretical formula is optimized, respectively. Then, an estimation formula based on maximum travel time similarity and an estimation formula based on maximum travel time confidence interval similarity are proposed, respectively. Finally, when analysing the fitting conditions, the collected data from urban roads in Nanjing are used to verify the proposed travel time estimation method based on the radio frequency identification devices. The results show that time headway deviation converges to zero when the hourly vehicle volume is more than 20 veh/h in the certain flow direction, and there are more positive and negative fluctuations when the hourly vehicle volume is less than 10 veh/h in the certain flow direction. The accuracy of the proposed improved method based on typical vehicle travel time estimation is significantly improved by considering the typical vehicle travel time, and typical vehicles on the road segment mainly exist at the tail of the traffic platoon in the corresponding period.