Papers by Keyword: Travel Time

Abstract: Four-stage demand models are the most popular travel demand forecasting models. Trip assignment which is the last stage in the four-stage demand modelling is a key element in travel demand forecasting process. Traffic assignment model is used to assign travel demands into the road network and predict network flows that are associated with future planning scenarios based on the estimates of link travel times. In order to calculate travel time between origin and destination, a function presenting the relationship between link delays and link flows is used. This function is known as Volume-Delay Function (VDF) and it is the fundamental component of equilibrium trip assignment models. This study aims to investigate and improve VDFs for heterogeneous traffic at different type of arterial roads in Malaysia by using the road network in Balik Pulau, Penang as a case study. Primary data such as traffic volume and speed are collected at three types of arterial roads, which are the principal arterial, minor arterial and collectors to derive the parameters required in the VDFs. In this study, the most well-known and most widely-used volume-delay function which is known as the BPR function developed by the U.S. Bureau of Public Roads was investigated and calibrated using the transportation planning software, EMME 4.1.3. The calibrated functions are then validated with field data. The output from this research is very important as better understanding of VDFs can produce better estimate of link travel times and hence better planning for future scenarios.

Abstract: With the development of the economy, people have higher request for the time. Studying the choice of travel about rail passengers becomes more significant. Due to these problem above and using survival analysis method, this paper builds travel time survival model based on questionnaire and have a whole analysis of the travel time of the rail passenger. Then, it concludes that most of the rail passengers’ travel time is below five hours. At last, this paper builds COX proportional hazard rate model of travel time and study the factors about travel time. The result demonstrates that the factor about whether it is students or not, family income, whether it is travelling and the number of packages has a significant influence on the travel time.

Abstract: The article proposed a new research. The method may avoid interference of reverse drive vehicle by installing back-to-back reader in section, adopting active electronic tags to expand the scope and accuracy about reading and writing, using the information including the number of electronic tags in scanning section, update ratio, residence time to build the linkage system between control system and vehicle-mounted electronic tags, and cooperating with the signal condition in intersection, to make the discrimination of traffic state, especially the discrimination of congestion state. The method could identify the traffic state effectively, which was verified by simulated experiment through secondary development of VISSIM.

Abstract: Massive expressway toll data contained lots of valuable information. However, the skills of mining and analyzing toll data were limited currently. This study explored the modeling method of road network travel time reliability based on massive toll data. Firstly, this study obtained travel time data sample of each link at different months, and analyzed travel time statistical properties preliminarily. Secondly, this study used normal distribution, gamma distribution and Weibull distribution to fit travel time data sample, and different statistical indicators were involved to measure the fitting effect. Fitting results showed that normal distribution for link travel time was more rational and acceptable than the others. Thus, this study established link travel time reliability model, and proposed moment estimation method of calibrating the model parameters. In practical application, the reliability model can be used to judge traffic operating posture for expressway management department, and also can be used to forecast travel time information, to provide valuable reference on decision-making for drivers travel plan or route choice.

Abstract: It is recognized that travel times on a link are temporally correlated with its travel times of previous time periods. Also, the link travel time are spatially correlated by travel times on its neighboring links. Based on such temporal and spatial correlations, a new method is proposed for travel time prediction in urban roads. The proposed method is capable of rapidly predicting the link travel time in the near future. For validation of the proposed method, the temporal and spatial variance-covariance of travel times on related links are employed together with historical travel time data. It is found that the proposed method is able to provide more accurate travel time prediction.

Abstract: This paper is concerned with the task of travel time pre-diction of urban roadway. For improving the travel time predication ac-curacy, a travel time predication model based multi-source data fusion is proposed. The prediction procedure is divided into two phases, the estimation phase and the prediction phase The method is combined the historical traffic patterns with real-time traffic data as a linear. The resulting model is tested with realistic traffic data, and is found to perform well.

Abstract: This paper discusses the implementation of a new travel time estimation method in a regional demand forecasting model. The developed model considers implicitly the influence of signal timing as a function of main street and cross street traffic demands, although signal timing setting is not required as input. The application presented in this paper demonstrates that the developed model is applicable to a large network without the burden of signal timing input requirement. The results indicate that the application of the model can improve the performance of traffic assignment as part of the demand forecasting process. The model is promising to support dynamic traffic assignment (DTA) model applications in the future.

Abstract: Travel time reliability, as a measure of performance, is attracting more and more attention because unreliable transportation information hinders travelers’ decision making and creates difficulties for authorities to manage network operations. Since travel time reliability is closely related to the stochastic properties of the day-to-day travel time distribution, several statistical measures have been proposed, including standard deviation, coefficient of variation, buffer index, misery index and so on. Each of these measures is derived from travel time distribution but captures only one or two characteristics of travel time. In this paper, an effort is made to evaluate travel time reliability incorporating as many characteristics of travel time as possible based on fuzzy logic. The basic rules are: (1) the larger the variance is, the more unreliable the travel time is; (2) the larger the travel times of unlucky travelers are, the more unreliable the travel time is; (3) the larger the distribution skews to the left, the more unreliable the travel time is. The proposed methodology has been tested and analyzed with field data.

Abstract: The present paper is to analyses the key parameters of multi-reflection in GPR Survey. At first, it introduced the theory and methods for GPR result explanation. Then it proposed the necessity to work on multi-reflection analysis. For the complexity of multi-reflection, it emphatically worked on secondary reflection. According to the distinction of travel routes, the secondary reflection was divided into two cases. In the first case, the incident wave reflected twice at every interface and in the second case, the incident wave reflected for twice at every layer, meanwhile, the second reflected wave penetrated into the next layer as new incident wave. In these two cases, the amount of received waves was different and their corresponding transmitting parameters were deduced in the present paper. According to the deduction, in the situation of multiple reflections, the two-way travel time increases gradually; the energy decreases sharply and the profile curves are similar to each other.

Abstract: For improving the travel time predication accuracy, a travel time predication model based multi-source historical is proposed. The model analyzes the different features between the loop detector data and the probing vehicles data, and creates traffic rules based on traffic patterns through data mining. Finally, the experiment of the navigation system based on multi-source historical data fusion is given. The results show the effectiveness of the model performs well.