Adaptive spatio-temporal mining for route planning and travel time estimation

2017 ◽  
Author(s):  
Rong Wen ◽  
Wenjing Yan ◽  
Allan N. Zhang
Keyword(s):  
Travel Time ◽  
Route Planning ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Temporal Mining ◽  
Spatio Temporal

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 Aggressive Driving Saves More Time? Multi-task Learning for Customized Travel Time Estimation

2019 ◽  
Author(s):  
Ruipeng Gao ◽  
Xiaoyu Guo ◽  
Fuyong Sun ◽  
Lin Dai ◽  
Jiayan Zhu ◽  
...  
Keyword(s):  
Travel Time ◽  
Fundamental Problem ◽  
Route Planning ◽  
Time Estimation ◽  
Location Based Services ◽  
Travel Time Estimation ◽  
Fine Grained ◽  
Task Learning ◽  
Deep Recurrent Neural Network ◽  
Gps Traces

Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy but they largely rely on GPS traces which are too coarse to model many personalized driving events. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS traces, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a link traffic database with topology representation, speed statistics, and query distribution. It also uses inertial data to estimate the arbitrary phone's pose in car, and detects fine-grained driving events. The multi-task learning structure predicts both traffic speed at public level and customized travel time at personal level. Extensive experiments on two real-world traffic datasets from Didi Chuxing have demonstrated our effectiveness.

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