Genetic Algorithm Based Efficient RSU Distribution to Estimate Travel Time for Vehicular Users

2015 ◽  
Author(s):  
Manipriya Sankaranarayanan ◽  
C. Mala ◽  
Samson Mathew
Keyword(s):  
Genetic Algorithm ◽  
Travel Time ◽  
Estimate Travel Time

Estimate travel time of ships in narrow channel based on AIS data

2020 ◽  
Vol 202 ◽  
pp. 106790 ◽  
Author(s):  
Xing Wu ◽  
Uttara Roy ◽  
Maryam Hamidi ◽  
Brian N. Craig
Keyword(s):  
Travel Time ◽  
Narrow Channel ◽  
Estimate Travel Time

scholarly journals Analysis of Component Errors in the Highway Capacity Manual Travel Time Reliability Estimations for Urban Streets

2020 ◽  
Vol 2674 (6) ◽  
pp. 85-97 ◽  
Author(s):  
Ernest O. A. Tufuor ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Estimate Travel Time ◽  
The Difference ◽  
Travel Time Variability

The Highway Capacity Manual 6th edition (HCM6) includes a new methodology to estimate and predict the distribution of average travel times (TTD) for urban streets. The TTD can then be used to estimate travel time reliability (TTR) metrics. Previous research on a 0.5-mi testbed showed statistically significant differences between the HCM6 estimated TTD and the corresponding empirical TTD. The difference in average travel time was 4 s that, while statistically significant, is not important from a practical perspective. More importantly, the TTD variance was underestimated by 70%. In other words, the HCM6 results reflected a more reliable testbed than field measurement. This paper expands the analysis on a longer testbed. It identifies the sources and magnitude of travel time variability that contribute to the HCM6 error. Understanding the potential sources of error, and their quantitative values, are the first steps in improving the HCM6 model to better reflect actual conditions. Empirical Bluetooth travel times were collected on a 1.16-mi testbed in Lincoln, Nebraska. The HCM6 methodology was used to model the testbed, and the estimated TTD by source of travel time variability was compared statistically to the corresponding empirical TTD. It was found that the HCM6 underestimated the TTD variability on the longer testbed by 67%. The demand component, missing variable(s), or both, which were not explicitly considered in the HCM6, were found to be the main source of the error in the HCM6 TTD. A focus on the demand estimators as the first step in improving the HCM6 TTR model was recommended.

scholarly journals Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2822 ◽  
Author(s):  
Chaoyang Shi ◽  
Bi Yu Chen ◽  
William H. K. Lam ◽  
Qingquan Li
Keyword(s):  
Data Fusion ◽  
Travel Time ◽  
Road Networks ◽  
Heterogeneous Data ◽  
Fusion Method ◽  
Estimate Travel Time

scholarly journals Genetic Algorithm for Multiple Bus Line Coordination on Urban Arterial

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Zhen Yang ◽  
Wei Wang ◽  
Shuyan Chen ◽  
Haoyang Ding ◽  
Xiaowei Li
Keyword(s):  
Genetic Algorithm ◽  
Travel Time ◽  
Public Transport ◽  
Major Part ◽  
Traffic Signals ◽  
Traffic Efficiency ◽  
Road Section ◽  
Bus Travel Time ◽  
The City ◽  
Urban Arterial

Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part ofZhongshan NorthStreet in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority.

Approach to estimate travel time using sparsely sampled GPS data in urban networks

2013 ◽  
Vol 49 (15) ◽  
pp. 957-958 ◽  
Author(s):  
A. Hadachi ◽  
S. Mousset ◽  
A. Bensrhair
Keyword(s):  
Travel Time ◽  
Gps Data ◽  
Urban Networks ◽  
Estimate Travel Time
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