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 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 Traffic Congestion in Dar es Salaam: Implications for Workers’ Productivity

2016 ◽  
Vol 9 (6) ◽  
pp. 103 ◽  
Author(s):  
Hosea Mpogole ◽  
Samira Msangi
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Dar Es Salaam ◽  
Transport Systems ◽  
Peak Period ◽  
Traffic Jam ◽  
Average Travel Time ◽  
Commuting Time ◽  
Travel Time Index ◽  
The City

Inadequately planned transport systems result to traffic congestion, a challenge that has for long been a thorn in Dar es Salaam, the city most affected in Tanzania. Although traffic congestion has been a major concern in Dar es Salaam, marked reluctance has been noticed in taking measures towards a lasting solution thus, it is of diminutive surprise that limited studies and documentations on the same are in existence. Therefore, this study assesses traffic congestion in Dar es Salaam and particularly its implications for workers’ productivity. Travel time and productivity indexes were established from a sample of 96 workers who used public transport along Morogoro and Mandela Roads. Travel time index (TTI) is the ratio of the average travel time during peak period to the travel time during off-peak period. Findings reveal that TTI was 2.19. Workers spent about 2 times of the average commuting time to work and 3 times of the same commuting from work to their various residences. About 2.5 hours were lost on traffic jam per day and that people worked 1.4 times less than the required time due to traffic congestion. It was further established that in 10 working days, almost 3 days were lost to traffic congestion. Since there are ongoing efforts to improve the transport system through the Bus Rapid Transit (BRT) project, it remains to be seen as to what extent traffic congestion will be reduced. In either case, this study provides a benchmark for comparisons.

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.

scholarly journals Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics

2021 ◽  
Author(s):  
Sarvani Duvvuri ◽  
Srinivas S. Pulugurtha
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Congestion ◽  
Pearson Correlation ◽  
Time Data ◽  
Network Characteristics ◽  
Time Index ◽  
Time Performance ◽  
Travel Time Data ◽  
Day Of The Week

Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.

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

scholarly journals Study on the Selection Model of Staying Adjustment Bus Lines along Rail Transit

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Cui ◽  
Yueer Gao ◽  
Jing Cheng ◽  
Lei Shi
Keyword(s):  
Decision Making ◽  
Travel Time ◽  
Public Transport ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
Gps Data ◽  
Rail Transit ◽  
Travel Time Savings ◽  
Time Savings ◽  
Selection Of

To fully achieve effective rail transit, prevent the waste of conventional bus capacity along a rail transit line, and relieve the urban traffic congestion problem, it is necessary to screen for the adjustment of conventional bus lines prior to the operation of rail transit to provide a basis for further optimization of bus lines. Based on the analysis of spatial relationships between a rail transit line and conventional collinear bus lines and considering the time advantage characteristics of rail transit in rush hours, a model of the generalized travel time costs and travel time savings proportion in the collinear section of rail transit and bus was proposed. To evaluate the utility of rail transit relative to conventional bus collinear lines, the conventional bus lines to be adjusted were determined. Taking Xiamen as an example, the bus lines of Hubin East Road Station as the endpoint of metro line 1 were employed to calculate the model using GPS data of the buses, and the bus lines to be adjusted in the Hubin East Road were determined. The results show that the model is effective in the elastic selection of conventional bus lines that need to be adjusted and provides decision-making support for urban comprehensive public transport planning.

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