Vehicles travelling on urban streets are heavily influenced by traffic signal controls, pedestrian crossings, and conflicting traffic from cross streets, which would result in bimodal travel time distributions, with one mode corresponding to travels without delays and the other travels with delays. A hierarchical Bayesian bimodal travel time model is proposed to capture the interrupted nature of urban traffic flows. The travel time distributions obtained from the proposed model are then considered to analyze traffic operations and estimate travel time distribution in real time. The advantage of the proposed bimodal model is demonstrated using empirical data, and the results are encouraging
Increasing mobility and congestion results in an increase in travel time variability and in a decrea...
Public transport travel time variability (PTTV) is essential for understanding deteriorations in the...
<p>a. An unimodal travel time distribution; b. An bimodal travel time distribution.</p
Urban travel times are intrinsically uncertain due to a lot of stochastic characteristics of traffic...
Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion est...
Urban travel times are rather variable as a result of a lot of stochastic factors both in traffic fl...
The rapid development and deployment of Intelligent Transportation Systems (ITSs) require the develo...
We introduce two statistical methods for estimating vehicle travel time distribu-tions on a road net...
Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest ...
Transfers and connections between lines in a public transport network are a major part of the planni...
AbstractTravel times can be directly estimated by measurements including probe vehicles, Bluetooth d...
AbstractThe probability distribution of travel time is the foundation of travel time estimation or m...
AbstractIncreasing mobility and congestion results in an increase in travel time variability and in ...
Public Transport Travel Time Variability (PTTV) is essential for understanding the deteriorations in...
Travel times in urban road networks are highly stochastic. However, most existing travel time estima...
Increasing mobility and congestion results in an increase in travel time variability and in a decrea...
Public transport travel time variability (PTTV) is essential for understanding deteriorations in the...
<p>a. An unimodal travel time distribution; b. An bimodal travel time distribution.</p
Urban travel times are intrinsically uncertain due to a lot of stochastic characteristics of traffic...
Despite the wide application of Floating Car Data (FCD) in urban link travel time and congestion est...
Urban travel times are rather variable as a result of a lot of stochastic factors both in traffic fl...
The rapid development and deployment of Intelligent Transportation Systems (ITSs) require the develo...
We introduce two statistical methods for estimating vehicle travel time distribu-tions on a road net...
Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest ...
Transfers and connections between lines in a public transport network are a major part of the planni...
AbstractTravel times can be directly estimated by measurements including probe vehicles, Bluetooth d...
AbstractThe probability distribution of travel time is the foundation of travel time estimation or m...
AbstractIncreasing mobility and congestion results in an increase in travel time variability and in ...
Public Transport Travel Time Variability (PTTV) is essential for understanding the deteriorations in...
Travel times in urban road networks are highly stochastic. However, most existing travel time estima...
Increasing mobility and congestion results in an increase in travel time variability and in a decrea...
Public transport travel time variability (PTTV) is essential for understanding deteriorations in the...
<p>a. An unimodal travel time distribution; b. An bimodal travel time distribution.</p