Using Automatic Vehicle Idenification Data for Travel Time Estimation in Hong Kong

Tam, Mei Lam; Lam, William H. K.

doi:10.1080/18128600808685688

Cited by 70 publications

(36 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the proposed method, the sensitive parameter η in Equations (12) and (15) was set as 0.2, which is initially recommended by Dion and Rakha [26], Tam and Lam [27]. Figure 7a shows the path travel times estimated by the proposed method against the observed path travel times.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In this study, the conventional moving average method is extended to obtain a robust estimation of bot mean and variances of travel times. The moving average method is adopted due to its simplicity and effectiveness [26,27]. Let t e ij,w−1 and t ij,w be the estimated link travel time at time interval w − 1 and link travel time at time interval w. Using the weighted moving average method, the estimated link travel time at time interval w, denoted by t e ij,w can be calculated by t e ij,w = α w · t ij,w + (1 − α w ) · t e ij,w−1 (11)…”

Section: The Weighted Moving Average Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Shi

Chen

2017

IJGI

View full text Add to dashboard Cite

Travel times in urban road networks are highly stochastic. However, most existing travel time estimation methods only estimate the mean travel times, while ignoring travel time variances. To this end, this paper proposes a robust travel time distribution estimation method to estimate both the mean and variance of travel times by using emerging low-frequency floating car data. Different from the existing studies, the path travel time distribution in this study is formulated as the sum of the deterministic link travel times and stochastic turning delays at intersections. Using this formulation, distinct travel time delays for different turning movements at the same intersection can be well captured. In this study, a speed estimation algorithm is developed to estimate the deterministic link travel times, and a distribution estimation algorithm is proposed to estimate the stochastic turning delays. Considering the low sampling rate of the floating car data, a weighted moving average algorithm is further developed for a robust estimation of the path travel time distribution. A real-world case study in Wuhan, China is carried out to validate the applicability of the proposed method. The results of the case study show that the proposed method can obtain a reliable and accurate estimation of path travel time distribution in congested urban road networks.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: The Weighted Moving Average Methodsmentioning

confidence: 99%

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Shi

Chen

2017

IJGI

View full text Add to dashboard Cite

show abstract

“…How to extend such method to the dynamic reliable path finding problems (e.g., Li et al [30]) is worthy of further investigation. (v) The proposed algorithm can be combined with some stochastic travel time estimation methods (Tam and Lam [31,32]; Chang et al [33]; Shao et al [34,35]) so as to provide a reliable route guidance system in reality. …”

Section: Conclusion and Future Studiesmentioning

confidence: 99%

The Global Optimal Algorithm of Reliable Path Finding Problem Based on Backtracking Method

Shen

Shao

Zhang

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user's risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the -shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.

show abstract

“…short-term travel time prediction and estimation and network reliability analysis in a similar way as Refs. [9][10][11][12][13]. The remainder of this paper is organized as followed.…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling of the equilibrium speed–density relationship

Wang

2011

J of Advced Transportation

View full text Add to dashboard Cite

SUMMARYAs the graphical and mathematical representation of relationships among traffic flow, speed, and density, the fundamental diagram has been the foundation of traffic flow theory and transportation engineering. Underlying the fundamental diagram is the speed-density relationship which was originally documented in Greenshields' seminal work and followed by a variety of equilibrium models over the past 75 years. Observed in these efforts was their deterministic nature striving to pursue two seemingly competing goals: mathematical elegance and empirical accuracy, the former of which is attractive to mathematical modeling of traffic dynamics, and the latter is required if such modeling is meant to be realistic. As a continued effort of such a pursuit, this paper presents a stochastic speed-density model. The motivation is twofold: first, it is desirable to have a model which achieves both goals reasonably and second, the stochastic model can potentially lead to probabilistic traffic flow modeling and prediction which is typically not offered by a macroscopic approach.

show abstract

Using Automatic Vehicle Idenification Data for Travel Time Estimation in Hong Kong

Cited by 70 publications

References 10 publications

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

The Global Optimal Algorithm of Reliable Path Finding Problem Based on Backtracking Method

Stochastic modeling of the equilibrium speed–density relationship

Contact Info

Product

Resources

About