Comparison of several traffic forecasting methods based on travel time index data on weekends

2010 ◽  
Vol 15 (2) ◽  
pp. 188-193
Author(s):  
Yang Zhang ◽  
Wen-huan Shi ◽  
Yun-cai Liu
Keyword(s):  
Travel Time ◽  
Traffic Forecasting ◽  
Time Index ◽  
Index Data ◽  
Forecasting Methods ◽  
Travel Time Index

scholarly journals A weighted travel time index based on data from Uber Movement

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Renato S. Vieira ◽  
Eduardo A. Haddad
Keyword(s):  
Travel Time ◽  
Time Index ◽  
Travel Time Index

scholarly journals Enhanced Magnetic Wireless Sensor Network Algorithm for Traffic Flow Monitoring in Low-Speed Congested Traffic

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Haji Said Fimbombaya ◽  
Nerey H. Mvungi ◽  
Ndyetabura Y. Hamisi ◽  
Hashimu U. Iddi
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Moving Average ◽  
Wireless Sensor ◽  
Decision Algorithm ◽  
Low Speed ◽  
Congested Traffic ◽  
Flow Monitoring ◽  
Time Index ◽  
Travel Time Index

Traffic flow monitoring using magnetic wireless sensor networks in chaotic cities of developing countries represents an emergent technology. One of the challenges facing such deployment is the development of effective detection signal-processing algorithm in low-speed congested traffic based on the Earth’s magnetic fields. The proposed algorithm is the performance improvement of the previous algorithm known as the Scanning and Decision Algorithm (SDA). The novel algorithm based on the moving-average model includes an addition of a two-pass moving-average filter to improve the signal-to-noise ratio after analog-to-digital conversion. The improved mathematical capabilities enable us to capture additional features of vehicular direction and classification. Other outputs of the model include vehicular detection, count, speed, and travel time index (TTI). The performance evaluation of a proposed algorithm is conducted through on-site real-time experiments at the designated road segment. The results indicated that the roadside magnetic sensor improved vehicular detection, count, travel time index, and classification during low-speed congested traffic state.

Measuring Traffic Congestion with Taxi GPS Data and Travel Time Index

CICTP 2015 ◽  
2015 ◽  
Author(s):  
Xiangfu Kong ◽  
Jiawen Yang ◽  
Zhongyu Yang
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Gps Data ◽  
Time Index ◽  
Travel Time Index

scholarly journals TRAVEL TIME RELIABILITY INDICES FOR URBAN ROUTES IN BAGHDAD CITY

2021 ◽  
Vol 25 (5) ◽  
pp. 1-14
Author(s):  
Estabraq F. Alattar ◽  
◽  
Zainab A. alkaissi ◽  
Ali J. Kadem ◽  
◽  
...  
Keyword(s):  
Travel Time ◽  
Travel Time Reliability ◽  
Reliability Indices ◽  
Time Index ◽  
North Direction ◽  
Buffer Time ◽  
Gps Devices ◽  
Travel Time Index ◽  
Areas Of Interest

Reliability is one of the main metrics of transport system efficiency and quality of service. For both travelers and transport management organizations, the high variance of road travel times has become a problem. Reliability has been identified as one of the main areas of interest of the Strategic Highway Research Plan II. In order to evaluate congestion and unexpected changes in travel time, reliability metrics are increasingly used. GPS devices provide for exact assessment of travel time for each connection along the routes used for this research. (14 Ramadan arterial street, Al-Karada arterial street and Damascus arterial street). A GPS-equipped instrumented car was used to gather 50 test runs at peak and off peak times. At peak and off peak hours, 50 test runs were obtained using a GPS-equipped instrumented car. Raising the buffer time index results in inferior conditions for reliability. A buffer index of AL- Karada street was created about 53% and 30% for Damascus street and finally for 14 Ramadan street which present a 29% buffer index for north direction. As for its southern direction 14 Ramadan street created a buffer index of about 65% and 33% for AL- Karada street and finally for Damascus street which present a 29% buffer index. In addition, travel time index for (14 Ramadan street, AL- Karada street and Damascus street) respectively is about 2.8 %, 3.3% and 2.6% for north direction, as for its southern direction the travel time index is obtained for (14 Ramadan street, AL- Karada street and Damascus street) respectively were a 3%,3.7%, and 2.5%. Finally, the 95% percentile travel time for observed three selected routes in this study, the extra delay was felt on each route (1627, 2212, and 1192) sec. for (14 Ramadan street, AL- Karada street and Damascus street) for north direction, as for its southern direction the extra delay that perceived on each route (2221, 2132, and 975) sec. for (14 Ramadan street, AL- Karada street and Damascus street) respectively.

scholarly journals Empirical Analysis for Measuring Travel Time Reliability on Road Network

2021 ◽  
Vol 23 (2) ◽  
pp. 100-107
Author(s):  
Muhammad Karami ◽  
Dwi Herianto ◽  
Siti A. Ofrial ◽  
Ning Yulianti
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Effects ◽  
Travel Time Reliability ◽  
Planning Time ◽  
The Road ◽  
Time Index ◽  
Buffer Time ◽  
Time Buffer ◽  
Travel Time Index

This research analyses the characteristics of travel time reliability for the road network in Kota Bandar Lampung. Therefore, travel time consists of access, wait and interchange time, while its reliability deals with variations of in-passenger/private cars time. Survey of travel time on each road was carried out for 12 hours (from 06.00 to 18.00) for five working days. Furthermore, the buffer time method was used to measure the characteristics of time travel reliability consisting of five measuring tools, namely planning time, planning time index, buffer time, buffer time index and travel time index. This research found that the temporal effects are the main factor that tends to affect travel time, whereas network effects are the second factor that tends to affect travel time. Furthermore, the regression equation was developed to express the effect of planning time (TPlan) and free-flow travel time on average travel time .

Seq2Img-DRNET: A travel time index prediction algorithm for complex road network at regional level

2021 ◽  
pp. 115554
Author(s):  
Xiujuan Xu ◽  
Yuzhi Sun ◽  
Yulin Bai ◽  
Kai Zhang ◽  
Yu Liu ◽  
...  
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Regional Level ◽  
Prediction Algorithm ◽  
Time Index ◽  
Travel Time Index

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

2021 ◽  
pp. 036119812110315
Author(s):  
Markus Steinmaßl ◽  
Stefan Kranzinger ◽  
Karl Rehrl
Keyword(s):  
Travel Time ◽  
Time Of Day ◽  
Temporal Aggregation ◽  
Travel Time Reliability ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Depth Analysis ◽  
Data Source ◽  
Travel Time Index

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

Sign in / Sign up

Export Citation Format

Share Document