scholarly journals Underground travel time estimation for macro simulation models, on the example of the first line in Warsaw

2014 ◽  
Vol 13 (4) ◽  
pp. 067-074
Author(s):  
Marek Bauer
Keyword(s):  
Travel Time ◽  
Stopping Time ◽  
Time Estimation ◽  
Simulation Models ◽  
Stopping Times ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Running Time ◽  
Average Running Time ◽  
The Impact

In this paper, the author’s model of underground travel time prediction was presented. The structure of the model can be used to estimate the travel time of any underground line in the planning phase. The model takes into account the length and the variability of running time between stations and stopping time at these stations. Partial models of average running time depending on the length of the sections – for six periods of the working day, developed on the basis of measurements on the first underground line in Warsaw were presented. Similar models for the estimation of the standard deviation of running time were also presented. Stopping times for three types of stations, varying in terms of the average stopping time and location of the station in relation to the city center were estimated. Paper presents also a practical example of the use of the model: evaluation of the impact of additional stations on the travel time on the underground line in Warsaw.

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.

Dynamic Travel Time Estimation Techniques for Urban Freight Transportation Networks Using Historical and Real-Time Data

2011 ◽  
pp. 252-273
Author(s):  
Vasileios Zeimpekis
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Real Life ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Time Data ◽  
Urban Freight ◽  
Real Time Traffic ◽  
Real Time Data

Effective travel time prediction is of great importance for efficient real-time management of freight deliveries, especially in urban networks. This is due to the need for dynamic handling of unexpected events, which is an important factor for successful completion of a delivery schedule in a predefined time period. This chapter discusses the prediction results generated by two travel time estimation methods that use historical and real-time data respectively. The first method follows the k-nn model, which relies on the non-parametric regression method, whereas the second one relies on an interpolation scheme which is employed during the transmission of real-time traffic data in fixed intervals. The study focuses on exploring the interaction of factors that affect prediction accuracy by modelling both prediction methods. The data employed are provided by real-life scenarios of a freight carrier and the experiments follow a 2-level full factorial design approach.

Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway

2003 ◽  
Vol 1855 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Chumchoke Nanthawichit ◽  
Takashi Nakatsuji ◽  
Hironori Suzuki
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Traffic Information ◽  
Estimation Accuracy ◽  
Travel Time Estimation ◽  
Travel Time Prediction ◽  
Short Term ◽  
State Equations ◽  
Probe Vehicles ◽  
Real Time Traffic

Traffic information from probe vehicles has great potential for improving the estimation accuracy of traffic situations, especially where no traffic detector is installed. A method for dealing with probe data along with conventional detector data to estimate traffic states is proposed. The probe data were integrated into the observation equation of the Kalman filter, in which state equations are represented by a macroscopic traffic-flow model. Estimated states were updated with information from both stationary detectors and probe vehicles. The method was tested under several traffic conditions by using hypothetical data, giving considerably improved estimation results compared to those estimated without probe data. Finally, the application of the proposed method was extended to the estimation and short-term prediction of travel time. Travel times were obtained indirectly through the conversion of speeds estimated or predicted by the proposed method. Experimental results show that the performance of travel-time estimation or prediction is comparable to that of some existing methods.

Determining the Number of Probe Vehicles for Freeway Travel Time Estimation by Microscopic Simulation

2000 ◽  
Vol 1719 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Mei Chen ◽  
Steven I. J. Chien
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Accurate Estimate ◽  
Traffic Information ◽  
Travel Time Estimation ◽  
Probe Vehicles ◽  
Real Time Traffic ◽  
Minimum Number ◽  
Link Travel Time ◽  
The Impact

Using probe vehicles to collect real-time traffic information is considered an efficient method in real-world applications. How to determine the minimum number of probe vehicles required for accurate estimate of link travel time is a question of increasing interest. Although it usually is assumed that link travel time is normally distributed, it is shown, on the basis of simulation results, that sometimes this is not true. A heuristic of determining the minimum number of probe vehicles required is developed to accommodate this situation. In addition, the impact of traffic volume on the required probe vehicle number is discussed.

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.

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