scholarly journals Analysis of Traffic Conditions in Urban Region Based on Data from Fixed Detectors

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shidong Liang ◽  
Shuzhi Zhao ◽  
Minghui Ma ◽  
Huasheng Liu
Keyword(s):  
Traffic Control ◽  
Evaluation System ◽  
Travel Speed ◽  
Urban Region ◽  
Speed Estimation ◽  
Network Efficiency ◽  
Estimation Model ◽  
Fundamental Diagram ◽  
Traffic Conditions ◽  
Macroscopic Fundamental Diagram

To evaluate traffic conditions in an urban region, the average travel speed in the street network and the average traffic flow per lane are selected as indexes. The results for identifying traffic conditions are obtained using macroscopic fundamental diagram. Because of the difficulty of collecting the traffic parameter of travel speed from widely distributed fixed detectors directly, in this paper, the relationship between average travel speed and queue lengths at signalized intersections within the urban area is established based on an equivalent assumption. Finally, the average travel speed estimation model proposed in this paper is tested with microscopic simulation platform, and the results showed that the average travel speed estimation model performed well with satisfactory accuracy. In addition, the best performance of the network efficiency can be identified based on the reflections of macroscopic fundamental diagram, and the evaluation system is simple enough for application in engineering. Therefore, the result is instructive both for generating traffic control strategies and for making route choices.

scholarly journals Introducing a Re-Sampling Methodology for the Estimation of Empirical Macroscopic Fundamental Diagrams

2018 ◽  
Vol 2672 (20) ◽  
pp. 239-248 ◽  
Author(s):  
Lukas Ambühl ◽  
Allister Loder ◽  
Michiel C. J. Bliemer ◽  
Monica Menendez ◽  
Kay W. Axhausen
Keyword(s):  
Traffic Control ◽  
Critical Density ◽  
Network Capacity ◽  
Accurate Estimation ◽  
Fundamental Diagram ◽  
Data Set ◽  
Urban Dynamics ◽  
Traffic Conditions ◽  
Macroscopic Fundamental Diagram ◽  
Inaccurate Estimation

The uncertainty in the estimation of the macroscopic fundamental diagram (MFD) under real-world traffic conditions and urban dynamics might result in an inaccurate estimation of the MFD parameters—especially if congestion is rarely observed network-wide. For example, as data normally come from punctual observations out of the whole network, it is unclear how representative these observations might be (i.e., how much is the observed capacity affected by the network’s inhomogeneity). Similarly, if the observed data do not exhibit a distinct congested branch, it is hard to determine the network capacity and critical density. This, in turn, also leads to uncertainties and errors in the parametrization of the MFD for applications, for example traffic control. This paper introduces a novel methodology to estimate (i) the level of inhomogeneity in the network, and (ii) the critical density of the MFD, even when no congested branch is observed. The methodology is based on the idea of re-sampling the empirical data set. Using an extensive data set from Lucerne, Switzerland, and London, UK, insights are provided on the performance and the application of the proposed methodology. The proposed methodology is used to illustrate how the level of inhomogeneity is lower in Lucerne than in the three areas of the network of London that are investigated. The proposed measure of the level of inhomogeneity gives city planners the possibility to analyze and investigate how efficiently their road network is utilized. In addition, the analysis shows that, for the network of Lucerne, the proposed methodology allows accurate estimation of the critical density up to 16 times more often than would be possible otherwise. This simple and robust estimation of the critical density is crucial for the application of many traffic control algorithms.

scholarly journals Simultaneous Optimization of Vehicle Arrival Time and Signal Timings within a Connected Vehicle Environment

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 191 ◽  
Author(s):  
Wei Wu ◽  
Ling Huang ◽  
Ronghua Du
Keyword(s):  
Traffic Control ◽  
Arrival Time ◽  
Compliance Rate ◽  
Travel Speed ◽  
Simultaneous Optimization ◽  
Signal Timing ◽  
Connected Vehicle ◽  
Traffic Conditions ◽  
Proposed Model ◽  
Guidance Model

Most existing signal timing plans are optimized given vehicles’ arrival time (i.e., the time for the upcoming vehicles to arrive at the stop line) as exogenous input. In this paper, based on the connected vehicle (CV) technique, vehicles can be regarded as moving sensors, and their arrival time can be dynamically adjusted by speed guidance according to the current signal status and traffic conditions. Therefore, an integrated traffic control model is proposed in this study to optimize vehicle arrival time (or travel speed) and signal timing simultaneously. “Speed guidance model at a red light” and “speed guidance model at a green light” are presented to model the influences between travel speed and signal timing. Then, the methods to model the vehicle arrival time, vehicle delay, and number of stops are proposed. The total delay, which includes the control delay, queuing delay, and signal delay, is used as the objective of the proposed model. The decision variables consist of vehicle arrival time, starting time of green, and duration of green for each phase. The sliding time window is adopted to dynamically tackle the problems. Compared with the results optimized by the classical actuated signal control method and the fixed-time-based speed guidance model, the proposed model can significantly decrease travel delays as well as improve the flexibility and mobility of traffic control. The sensitivity analysis with the communication distance, the market penetration of connected vehicles, and the compliance rate of speed guidance further demonstrates the potential of the proposed model to be applied in various traffic conditions.

Individual Truck Speed Estimation from Advanced Single Inductive Loops

2019 ◽  
Vol 2673 (5) ◽  
pp. 272-284
Author(s):  
Yiqiao Li ◽  
Andre Y. C. Tok ◽  
Stephen G. Ritchie
Keyword(s):  
Essential Element ◽  
Feature Weighting ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Estimation Model ◽  
Inductive Loop ◽  
Traffic Conditions ◽  
Loop Detector ◽  
Inductive Loop Detector ◽  
The Individual

Trucks are an essential element in freight movements, transporting 73% of freight tonnage among all modes. However, they are also associated with severe adverse impacts on roadway congestion, safety, and air pollution. Truck speed by truck body types has been considered as an indicator of traffic conditions and roadway emissions. Even though vehicle speed estimation has been researched for decades, there exists a gap in estimating truck speeds particularly at the individual vehicle level. The wide diversity of vehicle lengths associated with trucks makes it especially challenging to estimate truck speeds from conventional inductive loop detector data. This paper presents a new speed estimation model which uses detailed vehicle signature data from single inductive loop sensors equipped with advanced detectors to provide accurate truck speed estimates. This model uses new inductive signature features that show a strong correlation with truck speed. A modified feature weighting K-means algorithm was used to cluster vehicle length related features into 16 specific groups. Individual vehicle speed regression models were then developed within each cluster. Finally, a multi-layer perceptron neural network model was used to assign single loop signatures to the pre-determined speed related clusters. The new model delivered promising estimation results on both a truck-focused dataset and a general traffic dataset.

scholarly journals Application of Macroscopic Fundamental Diagram under Flooding Situation to Traffic Management Measures

2021 ◽  
Vol 13 (20) ◽  
pp. 11227
Author(s):  
Piyapong Suwanno ◽  
Rattanaporn Kasemsri ◽  
Kaifeng Duan ◽  
Atsushi Fukuda
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Road Network ◽  
Road Traffic ◽  
Control Measures ◽  
Flow Density ◽  
Fundamental Diagram ◽  
Urban Flood ◽  
Traffic Conditions ◽  
Macroscopic Fundamental Diagram

Bangkok, Thailand is prone to flooding after heavy rain. Many road sections become impassable, causing severe traffic congestion and greatly impacting activities. Optimal vehicle management requires the knowledge of flooding impact on road traffic conditions in specific areas. A method is proposed to quantify urban flood situations by expressing traffic conditions in specific ranges using the concept of macroscopic fundamental diagram (MFD). MFD-based judgement allows for a road manager to understand the current traffic situation and take appropriate traffic control measures. MFD analysis identified traffic flow–density and density–velocity relationships by using the shape of the estimated MFD travel time-series plots. Then, results were applied to develop a traffic model with vehicle-flow parameters as a measuring method for road-network performance. The developed model improved road-network traffic-flow performance under different flood conditions. A method is also presented for traffic management evaluation on the assumption that flooding occurs.

scholarly journals Real-time bus travel speed estimation model based on bus GPS data

2016 ◽  
Vol 8 (11) ◽  
pp. 168781401667816 ◽  
Author(s):  
Jiancheng Weng ◽  
Chang Wang ◽  
Hainan Huang ◽  
Yueyue Wang ◽  
Ledian Zhang
Keyword(s):  
Real Time ◽  
Travel Speed ◽  
Gps Data ◽  
Speed Estimation ◽  
Estimation Model ◽  
Model Based
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