Feasibility of Using Taxi Dispatch System as Probes for Collecting Traffic Information

Liu, Kai; Yamamoto, Toshiyuki; Morikawa, Taka

doi:10.1080/15472450802644447

Cited by 43 publications

(22 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wuhan road network local study area In our experiment, we choose one of the most commonly used indices, Pearson's coefficient [1,16], to quantitatively measure the spatial and temporal correlations of travel time. The Pearson correlation coefficient, giving a value between −1 and +1, is used to measure the linear correlation between two variables x and y.…”

Section: Definitionmentioning

confidence: 99%

“…Uncertainty is produced by fluctuations in traffic and affected by traffic control (e.g., due to incidents, road works and road geometry), weather conditions (e.g., due to temperature, rain, snow and wind), stochastic arrivals and departures at signalized intersections [1], and the travel direction of traffic flows. The influence of different factors on travel time is often complicated and hard to predict.…”

Section: Introductionmentioning

confidence: 99%

“…Those models however, were evaluated with data derived from vissim simulation model, not real GPS data reflecting traffic flows. Liu [1] proposed a model to estimate arterial travel time by tracing a virtual probe vehicle along an OD (Origin-Destination) route with multiple intersections. The model works quite well with very low estimation error of 1.8%.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple-Factor Based Sparse Urban Travel Time Prediction

et al. 2018

View full text Add to dashboard Cite

Abstract:The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

show abstract

Section: Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple-Factor Based Sparse Urban Travel Time Prediction

et al. 2018

View full text Add to dashboard Cite

show abstract

“…A recent study in Japan ( [18]) examined the feasibility of using taxis as probe vehicles. The results indicated that the frequency of probes was not high enough in order for the data to be useful.…”

Section: Stockholm Taxi Gps Data Analysismentioning

confidence: 99%

Requirements and potential of GPS-based floating car data for traffic management: Stockholm case study

Rahmani

Koutsopoulos

Ranganathan

2010

13th International IEEE Conference on Intelligent Transportation Systems

View full text Add to dashboard Cite

The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. The paper presents the development of a laboratory designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control. It also presents results to illustrate the scope of traffic information that can be provided by GPS-based data, using the city of Stockholm as a case study. The preliminary analysis shows that network coverage, especially during peak weekday hours, is adequate. Further investigation is needed to validate the data, and increase its value through fusion with complementary data from other sources.

show abstract

“…Uncertainty is produced by fluctuations in traffic and affected by many other factors, such as traffic demand (e.g., due to population characteristics, seasonal effects, time instant, driver behavior, the availability of traffic information, and user responses), traffic control (e.g., due to accidents, road work, and road geometry), weather conditions (e.g., due to temperature, rain, snow, and wind), stochastic arrivals and departures at signalized intersections [1], and the travel direction of traffic flows. These random fluctuations are often complicated and difficult to predict.…”

Section: Introductionmentioning

confidence: 99%

Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

Zhang

Zhu

Hu³

et al. 2016

IJGI

View full text Add to dashboard Cite

Abstract:The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient-boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient-boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient-boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction.

show abstract

Feasibility of Using Taxi Dispatch System as Probes for Collecting Traffic Information

Cited by 43 publications

References 13 publications

Multiple-Factor Based Sparse Urban Travel Time Prediction

Multiple-Factor Based Sparse Urban Travel Time Prediction

Requirements and potential of GPS-based floating car data for traffic management: Stockholm case study

Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

Contact Info

Product

Resources

About