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 .

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.

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

Statistical Modeling of Quartiles, Standard Deviation, and Buffer Time Index of Optimal Tour in Traveling Salesman Problem and Implications for Travel Time Reliability

2020 ◽  
Vol 2674 (12) ◽  
pp. 339-347
Author(s):  
Hongtai Yang ◽  
Xiuqin Liang ◽  
Zhaolin Zhang ◽  
Yugang Liu ◽  
Malik Muneeb Abid
Keyword(s):  
Standard Deviation ◽  
Travel Time ◽  
Shortest Path ◽  
Shortest Paths ◽  
Service Area ◽  
Traveling Salesman ◽  
Travel Time Reliability ◽  
Time Index ◽  
Buffer Time ◽  
Number Of Customers

The traveling salesman problem (TSP) plays an important role in the field of transportation and logistics. While most studies focus on developing algorithms to find the shortest path and explore the average length of the shortest paths, the degree to which the shortest path deviates from its mean has not been studied. The study of deviation is important because it has implications for travel time reliability. Previous studies have used various indicators to measure this deviation, mainly including standard deviation, quantiles, and buffer time index (BTI). Therefore, this study aims to develop an empirical model to estimate the standard deviation, quantiles, and BTI for the optimal TSP tour. Experiments are performed to find the shortest path connecting N customers, which are generated randomly in a specified service area, using a genetic algorithm. The service area is a rectangle with ratio of length to width ranging from 1:1 to 8:1. Two types of lengths are considered: Euclidean and Manhattan. The number of customers considered ranges from 10 to 100 with intervals of 10. In the experimental design, the customers are generated randomly 500 times. The quartiles and standard deviations of the 500 shortest paths are recorded. The BTI is also calculated. Regression models are developed to estimate quartiles, standard deviation, and BTI using number of customers and parameters of service area as predictive variables. The models perform well on the testing data set. The constructed models can be used to estimate the standard deviation and reliability of travel time.

scholarly journals Development and Usage of Travel Time Reliability Model for Urban Road Network under Ice and Snowfall Conditions

1970 ◽  
Vol 24 (5) ◽  
pp. 395-403 ◽  
Author(s):  
Jun-Qiang Leng ◽  
Yu-Qin Feng ◽  
Ya-Ping Zhang ◽  
Yi He
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Reliability ◽  
Weighted Average ◽  
Evaluation Methodology ◽  
Correction Function ◽  
Reliability Model ◽  
Travel Time Reliability ◽  
The Road ◽  
Set Up

This paper discusses the travel time reliability of road network under ice and snowfall conditions. With the introduction of correction function for the influence of ice and snowfall conditions on free travel time and capacity, the function of travel time was established. According to the limitation of the current travel time reliability, the new definition was defined on the basis of quantifying the relationship between LOS (Level of Service) and travel time reliability. The breakthrough of the traditional idea that the route travel time reliability model was set by general series system was made by considering the route as a whole unit; instead of using a paralleling system; another breakthrough was made to calculate the weighted average travel time reliability of OD (Original Destination) pair. On the basis of OD pair travel time reliability, the road network reliability model was set up. A partial road network was taken as an example to validate the effectiveness and practicality of the evaluation methodology.

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.

scholarly journals Macroscopic dynamics and the collapse of urban traffic

2018 ◽  
Vol 115 (50) ◽  
pp. 12654-12661 ◽  
Author(s):  
Luis E. Olmos ◽  
Serdar Çolak ◽  
Sajjad Shafiei ◽  
Meead Saberi ◽  
Marta C. González
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Spatial Dynamics ◽  
Urban Traffic ◽  
Nonequilibrium Phase Transition ◽  
Vehicle Miles Traveled ◽  
The Road ◽  
Road Capacity ◽  
Total Vehicle ◽  
The Individual

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying Γ can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.

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 Passenger Travel Time Reliability for Multimodal Public Transport Journeys

2019 ◽  
Vol 2673 (2) ◽  
pp. 149-160 ◽  
Author(s):  
Malvika Dixit ◽  
Ties Brands ◽  
Niels van Oort ◽  
Oded Cats ◽  
Serge Hoogendoorn
Keyword(s):  
Travel Time ◽  
Network Reliability ◽  
Real Life ◽  
Travel Time Reliability ◽  
Behavioral Models ◽  
Buffer Time ◽  
Passenger Travel ◽  
Departure Time Choice ◽  
Multiple Routes ◽  
The Impact

Urban transit networks typically consist of multiple modes and the journeys may involve a transfer within or across modes. Therefore, the passenger experience of travel time reliability is based on the whole journey experience including the transfers. Although the impact of transfers on reliability has been highlighted in the literature, the existing indicators either focus on unimodal transfers only or fail to include all components of travel time in reliability measurement. This study extends the existing “reliability buffer time” metric to transit journeys with multimodal transfers and develops a methodology to calculate it using a combination of smartcard and automatic vehicle location data. The developed methodology is applied to a real-life case study for the Amsterdam transit network consisting of bus, metro, and tram lines. By using a consistent method for all journeys in the network, reliability can be compared between different transit modes or between multiple routes for the same origin–destination pair. The developed metric can be used to study the reliability impacts of policies affecting multiple transit modes. It can also be used as an input to behavioral models such as mode, route, or departure time choice models.

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