scholarly journals Travel Time Estimation Modelling under Heterogeneous Traffic: A Case Study of Urban Traffic Corridor in Surat, India

2018 ◽  
Vol 47 (4) ◽  
pp. 302-308 ◽  
Author(s):  
Krishna Saw ◽  
Aathira K. Das ◽  
Bhimaji K. Katti ◽  
Gaurang J. Joshi
Keyword(s):  
Travel Time ◽  
Heavy Traffic ◽  
Time Estimation ◽  
Urban Traffic ◽  
Heterogeneous Traffic ◽  
Travel Time Estimation ◽  
Estimation Model ◽  
Traffic Condition ◽  
Side Friction ◽  
The Impact

Achievement of fast and reliable travel time on urban road network is one of the major objectives for a transport planner against the enormous growth in vehicle population and urban traffic in most of the metropolitan cities in India. Urban arterials or main city corridors are subjected to heavy traffic flow resulting in degradation of traffic quality in terms of vehicular delays and increase in travel time. Since the Indian roadway traffic is characterized by heterogeneity with dominance of 2Ws (Two wheelers) and 3Ws (Auto rickshaw), travel times are varying significantly. With this in background, the present paper focuses on identification of travel time attributes such as heterogeneous traffic, road side friction and corridor intersections for recurrent traffic condition and to develop an appropriate Corridor Travel Time Estimation Model using Multi-Linear Regression (MLR) approach. The model is further subjected to sensitivity analysis with reference to identified attributes to realize the impact of the identified attributes on travel time so as to suggest certain measures for improvement.

scholarly journals DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision

2018 ◽  
Author(s):  
Hanyuan Zhang ◽  
Hao Wu ◽  
Weiwei Sun ◽  
Baihua Zheng
Keyword(s):  
Travel Time ◽  
Deep Neural Networks ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Urban Mobility ◽  
Time Cost ◽  
Trajectory Data ◽  
Estimation Model ◽  
New Development ◽  
Supervision Model

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to leverage the natural abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches. 

TAML: A Traffic-aware Multi-task Learning Model for Estimating Travel Time

2021 ◽  
Vol 12 (6) ◽  
pp. 1-14
Author(s):  
Jiajie Xu ◽  
Saijun Xu ◽  
Rui Zhou ◽  
Chengfei Liu ◽  
An Liu ◽  
...  
Keyword(s):  
Travel Time ◽  
Mutual Influence ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Mobility Patterns ◽  
Traffic Condition ◽  
Task Learning ◽  
Shared Space ◽  
Important Research Topic ◽  
Multiple Loss

Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize estimated traffic condition (by explicit features such as average traffic speed) for auxiliary supervision of travel time estimation, they fail to model their mutual influence and result in inaccuracy accordingly. To this end, in this article, we propose an improved traffic-aware model, called TAML, which adopts a multi-task learning network to integrate a travel time estimator and a traffic estimator in a shared space and improves the accuracy of estimation by enhanced representation of traffic condition, such that more meaningful implicit features are fully captured. In TAML, multi-task learning is further applied for travel time estimation in multi-granularities (including road segment, sub-path, and entire path). The multiple loss functions are combined by considering the homoscedastic uncertainty of each task. Extensive experiments on two real trajectory datasets demonstrate the effectiveness of our proposed methods.

Travel Time Estimation Based on Spot Speed with Instantaneous and Time Slice Model

2015 ◽  
Vol 776 ◽  
pp. 80-86
Author(s):  
Amirotul M.H. Mahmudah ◽  
A. Budiarto ◽  
S.J. Legowo
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Estimation Error ◽  
Time Estimation ◽  
Transportation Policy ◽  
Time Slice ◽  
Accurate Estimation ◽  
Travel Time Estimation ◽  
Traffic Condition ◽  
On Line

In off-line applications, travel time is the main parameter of road performance which can be the main consideration for evaluation and planning of transportation policy, and also to assess the accuracy of transportation modeling. While in on-line application travel time is main information for road users to define their travel behavior. Due to the important of travel time, therefore accurate estimation/prediction of travel time is essential. In order to fulfill it, this research analyzed the accuracy of Instantaneous and Time Slice model, and also evaluate the validity of Time mean speed and Space mean speed in mixed traffic condition. There is not much difference in travel time estimation error between models. The travel time estimation was larger than the actual travel time by floating car. It was also found that the error occurred on time mean speed are less than the space mean speed.

scholarly journals Travel Time Estimation Model for Emergency Vehicles under Preemption Control

2013 ◽  
Vol 96 ◽  
pp. 2147-2158 ◽  
Author(s):  
Jiawen Wang ◽  
Meiping Yun ◽  
Wanjing Ma ◽  
Xiaoguang Yang
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Estimation Model ◽  
Emergency Vehicles
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