scholarly journals Travel Time Estimation for Pedestrian with GPS Cell Phones as Probes

2018 ◽  
Vol 1 (1) ◽  
pp. 27-30
Author(s):  
Himal Acharya
Keyword(s):  
Kalman Filter ◽  
Travel Time ◽  
Cell Phones ◽  
Time Estimation ◽  
Time Of Day ◽  
Travel Time Estimation ◽  
Statistical Tool ◽  
Kalman Filter Algorithm ◽  
Map Data ◽  
Data Estimation

This paper estimates the travel time for pedestrian in Kist Medical Hospital- Balkumari route using cell phones’ GPS as probes. Using Google Map’s individual timeline, GPS data was traced for this route. Then, Kalman Filter Algorithm is used to estimate the travel time for pedestrian for that week day. Using algorithm result, statistical tool is used to measure the accuracy of travel time in particular origin-destination pair. Kalman filter algorithm is better approach for travel time estimation since the parameters get updated quickly if there is traffic fluctuation. Based on mean travel time, Kalman filter has better travel time estimation of 16.6 min with the help of historical data in compared to Google Map estimation of 18 min irrespective time of day in above origin-destination pair. Real observation is close to estimated travel time which signifies estimated travel time. Here author manages to compare the mean travel time between Kalman filter estimation and Google map data estimation.

scholarly journals Unscented Kalman filter for urban network travel time estimation

2012 ◽  
Vol 54 ◽  
pp. 1047-1057 ◽  
Author(s):  
Ré-Mi Hage ◽  
David Betaille ◽  
François Peyret ◽  
Dominique Meizel
Keyword(s):  
Kalman Filter ◽  
Travel Time ◽  
Unscented Kalman Filter ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Network

scholarly journals TRAVEL TIME ESTIMATION USING SPATIO-TEMPORAL INDEX BASED ON CASSANDRA

2018 ◽  
Vol IV-4 ◽  
pp. 235-242
Author(s):  
Z. Wu ◽  
C. Li ◽  
Y. Wu ◽  
F. Xiao ◽  
L. Zhu ◽  
...  
Keyword(s):  
Travel Time ◽  
Route Planning ◽  
Time Estimation ◽  
Traffic Monitoring ◽  
Spatial Index ◽  
Travel Time Estimation ◽  
Gps Trajectories ◽  
Spatio Temporal ◽  
Map Data ◽  
In Cells

<p><strong>Abstract.</strong> Travel time estimation plays an important role in traffic monitoring and route planning. Taxicabs equipped with Global Positioning System (GPS) devices have been frequently used to monitor the traffic state, and GPS trajectories of taxicabs also used to estimate path travel time in an urban area. However, in most cases, it is difficult to find a trajectory that fits perfectly with the query path, as some road segments may be traveled by no taxicab in present time slot. This makes it hard to estimate the travel time of the query path. This paper proposes a framework to estimate the travel time of a path by using the GPS trajectories of taxicabs as well as map data sources. In this framework, the travel time is represented as a series of residence time in cells (one cell is the gird segmentation unit), thus the key issues of the estimation are: finding the local traffic patterns of frequently shared paths from historical data and computing the stay time in cells. There are three major processes in this framework: trajectories preprocessing, establishing the temporal-spatial index and cell-based travel time estimation. Based on the temporal-spatial index, an algorithm is developed that uses similar route patterns, the cell-based travel time over a period of history and road network information to estimate the travel time of a path. This paper uses GPS trajectories of 10,357 taxicabs over a period of one week to evaluate the framework. The results demonstrate that this paper’s method is effective and feasible in city-wide scenarios.</p>

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.

Real-time Travel Time Estimation with Sparse Reliable Surveillance Information

2020 ◽  
Vol 4 (1) ◽  
pp. 1-23
Author(s):  
Wen Zhang ◽  
Yang Wang ◽  
Xike Xie ◽  
Chuancai Ge ◽  
Hengchang Liu
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Time Travel ◽  
Travel Time Estimation
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