scholarly journals Simulation Analyses of the Influence of Route and Travel Demand Properties on Traffic Assignment under the Travel Time Information

2006 ◽  
Vol 36 (4) ◽  
pp. 815-838
Author(s):  
Yasuyuki NAKAHIRA ◽  
Yasuhiro HIROBATA ◽  
Phyrum KOV
Keyword(s):  
Travel Time ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Time Information

scholarly journals Prediction of Commuter’s Daily Time Allocation

2013 ◽  
Vol 25 (5) ◽  
pp. 445-455 ◽  
Author(s):  
Fang Zong ◽  
Jia Hongfei ◽  
Pan Xiang ◽  
Wu Yang
Keyword(s):  
Support Vector Regression ◽  
Travel Time ◽  
Time Allocation ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Model System ◽  
Support Vector ◽  
Activity Duration ◽  
Wide Range ◽  
Duration Prediction

This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

scholarly journals Disparities in travel times between car and transit: Spatiotemporal patterns in cities

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuan Liao ◽  
Jorge Gil ◽  
Rafael H. M. Pereira ◽  
Sonia Yeh ◽  
Vilhelm Verendel
Keyword(s):  
Travel Time ◽  
Travel Demand ◽  
Time Of Day ◽  
Sao Paulo ◽  
São Paulo ◽  
Real World Data ◽  
Car Use ◽  
Real Time Traffic ◽  
Average Travel Time ◽  
Car Travel

AbstractCities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio $$R$$R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: $$R < 1$$R<1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.

Advanced Techniques for Travel Time Data Collection

1996 ◽  
Vol 1551 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Shawn M. Turner
Keyword(s):  
Data Collection ◽  
Travel Time ◽  
Real Time ◽  
Cellular Phone ◽  
License Plate ◽  
Time Data ◽  
Video Imaging ◽  
Advantages And Disadvantages ◽  
Travel Time Data ◽  
Time Information

Travel time information is becoming more important for applications ranging from congestion measurement to real-time travel information. Several advanced techniques for travel time data collection are discussed, including electronic distance-measuring instruments (DMIs), computerized and video license plate matching, cellular phone tracking, automatic vehicle identification (AVI), automatic vehicle location (AVL), and video imaging. The various advanced techniques are described, the necessary equipment and procedures are outlined, the applications of each technique are discussed, and the advantages and disadvantages are summarized. Electronic DMIs are low in cost but typically limited to congestion monitoring applications. Computerized and video license plate matching are more expensive and would be most applicable for congestion measurement and monitoring. Cellular phone tracking, AVI, and AVL systems may require a significant investment in communications infrastructure, but they can provide real-time information. Video imaging is still in testing stages, with some uncertainty about costs and accuracy.

scholarly journals Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Ge Gao ◽  
Zhen Wang ◽  
Xinmin Liu ◽  
Qing Li ◽  
Wei Wang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Field Study ◽  
Behavior Analysis ◽  
Travel Time ◽  
Travel Behavior ◽  
High Accuracy ◽  
Travel Distance ◽  
Distance Analysis ◽  
Trip Time ◽  
Time Information

Household traffic surveys are widely used in travel behavior analysis, especially in travel time and distance analysis. Unfortunately, any one kind of household traffic surveys has its own problems. Even all household traffic survey data is accurate, it is difficult to get the trip routes information. To our delight, electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, and Auto Navi Maps) could provide the trip route and time information, which remedies the traditional traffic survey’s defect. Thus, we can take advantage of the two kinds of data and integrate them into travel behavior analysis. In order to test the validity of the Baidu electric map API data, a field study on 300 taxi OD pairs is carried out. According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data. Based on the fused data of household traffic survey and electric map API, travel behavior on trip time and distance is analyzed. Results show that most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers. It is worth noting that students have the shortest travel distance and company business’s travel distance distribution is dispersed, which has the longest travel distance. Compared to travel distance, the standard deviations of all purposes’ travel time are greater than the travel distance. Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km.

scholarly journals A Dynamic Traffic Assignment Model for the Sustainability of Pavement Performance

2018 ◽  
Vol 11 (1) ◽  
pp. 170 ◽  
Author(s):  
Xinhua Mao ◽  
Jianwei Wang ◽  
Changwei Yuan ◽  
Wei Yu ◽  
Jiahua Gan
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Heavy Traffic ◽  
Pollutant Emissions ◽  
Pavement Performance ◽  
Dynamic Traffic ◽  
Teaching Learning ◽  
Pavement Damage

Existing Dynamic Traffic Assignment (DTA) models assign traffic flow with the principle of travel time, which are easy to distribute most of the traffic flows on the shortest path. A serious unbalance of traffic flow in the network can speed up pavement deterioration of highways with heavy traffic, which influences the sustainability of pavement performance and increases maintenance expenditures. The purpose of this research is to obtain a more optimized traffic assignment for pavement damage reduction by establishing a multi-objective DTA model with the objectives of not only minimum travel time but minimum decline of Present Serviceability Index (PSI) for pavements. Then, teaching-learning-based optimization (TLBO) algorithm is utilized to solve the proposed model. Results of a case study indicate that a more balanced traffic flow assignment can be realized by the model, which can effectively reduce average PSI loss, save maintenance expenditures, extend pavement service life span, save fuel consumption and reduce pollutant emissions in spite of a little increase of average travel time. Additionally, sensitivity of weight factor for the two objective functions is analyzed. This research provides some insights on methods on sustainable pavement performance.

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