scholarly journals TRAVEL TIME ANALYSIS OF SELECTED URBAN STREETS IN BAGHDAD CITY

2021 ◽  
Vol 25 (Special) ◽  
pp. 3-157-3-164
Author(s):  
Rania M. Ahmed ◽  
◽  
Zainab A. Alkaissi ◽  
Ruba Y. Hussain ◽  
◽  
...  
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Performance Metrics ◽  
Time Estimation ◽  
Travel Speed ◽  
Travel Time Estimation ◽  
Travel Time Reliability ◽  
Urban Streets ◽  
Traffic Performance ◽  
Local Speed

Estimating travel time and measuring speed are critical for increasing the efficiency and safety of traffic road networks. This study presents an investigation of arterial travel time estimation for vital routes in Baghdad city. These estimations including speeds, stops, and delays were computed via GPS device and compared to those currently used to quantify congestion and travel time reliability. The study involved a 45-day survey of private vehicles in Baghdad utilizing a Global Positioning System (GPS) probe to collect data on traffic performance metrics for analysis in a GIS context. It was found that the proposed travel time performance measures show definite differences in estimates of peak-hour travel time as compared with weekend travel time. Route (1) from Bayaa intersection - Bab Al-Mutham intersection (through highway) produced a travel time of 165 minutes and 136 minutes for Bayaa intersection - Bab Al-Mutham intersection (through downtown). The travel speed of routes 1 and 2 are observed near 25 kmph which is below the local speed limit of 70 kmph. The maximum travel time of routes 1 and 2 are 71 minutes and 37 minutes, respectively. While delay time was observed 45 and 20 minutes due to traffic congestion on route 1 and 2, respectively. The majority of vehicles are capable of traveling at normal speeds, with relatively few exceeding them.

scholarly journals Travel Time Estimation on Urban Street Segment

2018 ◽  
Vol 30 (1) ◽  
pp. 115-120 ◽  
Author(s):  
Jelena Kajalić ◽  
Nikola Čelar ◽  
Stamenka Stanković
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Speed ◽  
Degree Of Saturation ◽  
Level Of Service ◽  
Travel Time Estimation ◽  
Highway Capacity ◽  
Local Conditions ◽  
Highway Capacity Manual ◽  
Urban Streets

Level of service (LOS) is used as the main indicator of transport quality on urban roads and it is estimated based on the travel speed. The main objective of this study is to determine which of the existing models for travel speed calculation is most suitable for local conditions. The study uses actual data gathered in travel time survey on urban streets, recorded by applying second by second GPS data. The survey is limited to traffic flow in saturated conditions. The RMSE method (Root Mean Square Error) is used for research results comparison with relevant models: Akcelik, HCM (Highway Capacity Manual), Singapore model and modified BPR (the Bureau of Public Roads) function (Dowling - Skabardonis). The lowest deviation in local conditions for urban streets with standardized intersection distance (400-500 m) is demonstrated by Akcelik model. However, for streets with lower signal density (<1 signal/km) the correlation between speed and degree of saturation is best presented by HCM and Singapore model. According to test results, Akcelik model was adopted for travel speed estimation which can be the basis for determining the level of service in urban streets with standardized intersection distance and coordinated signal timing under local conditions.

Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

2019 ◽  
Vol 31 (02) ◽  
pp. 2050023
Author(s):  
Sida Luo
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Temporal Distribution ◽  
Time Estimation ◽  
Wave Model ◽  
Model Parameters ◽  
Travel Time Estimation ◽  
Time Model ◽  
Departure Time ◽  
Departure Time Choice

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

scholarly journals Possibilistic Approach for Travel Time Reliability Evaluation

2020 ◽  
Vol 6 (1) ◽  
pp. 223-243
Author(s):  
Rajesh S. Prabhu Gaonkar ◽  
Akshay V. Nigalye ◽  
Sunay P. Pai
Keyword(s):  
Travel Time ◽  
Fuzzy Variable ◽  
Time Estimation ◽  
Reliability Evaluation ◽  
Travel Time Estimation ◽  
Travel Time Reliability ◽  
Reliability Estimates ◽  
Data Imprecision ◽  
Transportation Vehicle ◽  
Reliability Computation

Travel time estimation & reliability evaluation of any means of transportation in every type of travel mode- land, rail, sea and air has been of immense interest of the researchers; primarily due to growing economic concern in the field of logistics & passenger movement. In situations like quantitative data inaccessibility or data imprecision, fuzzy set based possibilistic approach is recognized as a practical choice in obtaining the reliability estimates. This paper proposes and advocates possibilistic approach for travel time reliability computation of any type transportation vehicle under fuzzy type of data. The proposed approach is a novel way of computing the travel time & obtaining the related reliability value. Initially, the paper proposes the general methodology for travel time reliability evaluation. Individual travel time components of a transportation vehicle are considered as fuzzy; as a result, travel time is modelled as a fuzzy variable. Travel time reliability of a transportation vehicle has been defined with the help of possibilistic measures. The proposed procedure is then demonstrated with an application to marine vessel carrying the bulk. After illustration of the proposed methodology, sensitivity analysis is carried out. The paper ends with the comments on comparative features of the three cases.

Improved Travel Time Estimation for Reliable Performance Measure Development for Closed Highways

2015 ◽  
Vol 2526 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Hong Yang ◽  
Kaan Ozbay ◽  
Kun Xie
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Performance Measure ◽  
Sensor Data ◽  
Accurate Information ◽  
Travel Time Estimation ◽  
Travel Time Reliability ◽  
Estimate Travel Time ◽  
Reliable Performance ◽  
Toll Collection

Accurate travel time information not only is valuable for travelers but is critical to transportation agencies for quantifying the performance of their systems. Interest has been increasing in the development of reliable approaches for estimating travel time from various sensor data. Unlike the extensively studied estimation approaches based on point sensor measurements, the use of probe data from closed highway systems has been limited. To complement current understanding, this study developed an approach that used probe data from an electronic toll collection (ETC) system on closed freeways to estimate travel time. This approach differs from studies relying on automatic vehicle identification systems deployed on main lines as well as those estimated from point detectors. The proposed approach breaks down individual journey time into section travel time and fuses the probe data from vehicles that have used the links. The results, which are based on real-world case studies, illustrate the potential of mining ETC data for travel time estimation for both incident-free and incident conditions. In addition, the estimated results capture traffic dynamics better than instantaneous travel time estimates based on point sensor data. More accurate information is thus provided for deriving reliable performance measures to depict travel time reliability.

scholarly journals Vehicle Travel Time Estimation Using Sequence Prediction

2020 ◽  
Vol 32 (1) ◽  
pp. 1-12
Author(s):  
Gültekin Gündüz ◽  
Tankut Acarman
Keyword(s):  
Travel Time ◽  
Short Term Memory ◽  
Measurement Data ◽  
Prediction Method ◽  
Time Estimation ◽  
Absolute Error ◽  
Travel Speed ◽  
Travel Time Estimation ◽  
Velocity Change ◽  
Sequence Prediction

This paper proposes a region-based travel time and traffic speed prediction method using sequence prediction. Floating Car Data collected from 8,317 vehicles during 34 days are used for evaluation purposes. Twelve districts are chosen and the spatio-temporal non-linear relations are learned with Recurrent Neural Networks. Time estimation of the total trip is solved by travel time estimation of the divided sub-trips, which are constituted between two consecutive GNSS measurement data. The travel time and final speed of sub-trips are learned with Long Short-term Memory cells using sequence prediction. A sequence is defined by including the day of the week meta-information, dynamic information about vehicle route start and end positions, and average travel speed of the road segment that has been traversed by the vehicle. The final travel time is estimated for this sequence. The sequence-based prediction shows promising results, outperforms function mapping and non-parametric linear velocity change based methods in terms of root-mean-square error and mean absolute error metrics.

scholarly journals Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu
Keyword(s):  
Travel Time ◽  
Extreme Learning Machine ◽  
Road Networks ◽  
Time Estimation ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Travel Time Estimation ◽  
Trace Data ◽  
Learning Machine ◽  
Travel Time Forecasting

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

Sign in / Sign up

Export Citation Format

Share Document