Assessment of the Robustness of Signal Timing Plans in an Arterial Corridor Through Seasonal Variation of Traffic Flows

2017 ◽  
Vol 2619 (1) ◽  
pp. 85-94
Author(s):  
Marija Ostojic ◽  
Aleksandar Stevanovic ◽  
Dusan Jolovic ◽  
Hani S. Mahmassani
Keyword(s):  
Traffic Flow ◽  
New Technologies ◽  
The United States ◽  
Time Of Day ◽  
Traffic Information ◽  
Quality Data ◽  
Signal Timing ◽  
Traffic Demand ◽  
Traffic Conditions ◽  
Arterial Traffic

Arterial traffic signal systems, predominantly in the United States, deploy multiple signal timing plans to account for daily variability of traffic demand. Those types of traffic flow deviations should be anticipated when timing plans are designed and, therefore, serviced satisfactorily. When traffic flow patterns are no longer predictable, a predetermined time-of-day (TOD) plan may no longer be the optimal one. This research aimed to examine signal timing optimality by applying a method similar to the selection of a traffic responsive plan to recognize automatically the best timing plan suited to current traffic conditions. The proposed method attempted to determine whether the optimality of signal timing settings could have been effectively estimated when systematic detector counts of the major approach were available. The study used 4 months of data from field microwave detectors coupled with data of turning-movement counts obtained over several days. The findings show that TOD signal timing plans mainly depended on adequate data collection that best describes a specific set of traffic conditions. Thus, the designed plan was as optimal as the related traffic information was reliable, whereas a problem arose in the case of limited-availability and low-quality data. New technologies are capable of collecting and storing massive amounts of data. Even if the granularity of collected data is low, the data can be used to improve traffic performance (i.e., reduce corridor delay). This realization could be of particular importance to traffic agencies that have installed, or plan to install, new field devices.

scholarly journals Enhancing Freeway Safety through Intervening in Traffic Flow Dynamics Based on Variable Speed Limit Control

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jinming You ◽  
Shouen Fang ◽  
Lanfang Zhang ◽  
John Taplin ◽  
Jingqiu Guo
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
New Technologies ◽  
Flow Dynamics ◽  
Crash Risk ◽  
Speed Limit ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic Conditions ◽  
Safety Research

New technologies and traffic data sources provide great potential to extend advanced strategies in freeway safety research. The High Definition Monitoring System (HDMS) data contribute comprehensive and precise individual vehicle information. This paper proposes an innovative Variable Speed Limit (VSL) based approach to manage crash risks by intervening in traffic flow dynamics on freeways using HDMS data. We first conducted an empirical analysis on real-time crash risk estimation using a binary logistic regression model. Then, intensive microscopic simulations based on AIMSUN were carried out to explore the effects of various intervention strategies with respect to a 3-lane freeway stretch in China. Different speed limits with distinct compliance rates under specified traffic conditions have been simulated. By taking into account the trade-off between safety benefits and delay in travel time, the speed limit strategies were optimized under various traffic conditions and the model with gradient feedback produces more satisfactory performance in controlling real-time crash risks. Last, the results were integrated into lane management strategies. This research can provide new ideas and methods to reveal the freeway crash risk evolution and active traffic management.

Intelligent Traffic Signal Control Based on Reinforcement Learning with State Reduction for Smart Cities

2021 ◽  
Vol 21 (4) ◽  
pp. 1-24
Author(s):  
Li Kuang ◽  
Jianbo Zheng ◽  
Kemu Li ◽  
Honghao Gao
Keyword(s):  
Reinforcement Learning ◽  
Traffic Flow ◽  
Control Algorithm ◽  
The State ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
State Reduction ◽  
Traffic Demand ◽  
Matching Degree

Efficient signal control at isolated intersections is vital for relieving congestion, accidents, and environmental pollution caused by increasing numbers of vehicles. However, most of the existing studies not only ignore the constraint of the limited computing resources available at isolated intersections but also the matching degree between the signal timing and the traffic demand, leading to high complexity and reduced learning efficiency. In this article, we propose a traffic signal control method based on reinforcement learning with state reduction. First, a reinforcement learning model is established based on historical traffic flow data, and we propose a dual-objective reward function that can reduce vehicle delay and improve the matching degree between signal time allocation and traffic demand, allowing the agent to learn the optimal signal timing strategy quickly. Second, the state and action spaces of the model are preliminarily reduced by selecting a proper control phase combination; then, the state space is further reduced by eliminating rare or nonexistent states based on the historical traffic flow. Finally, a simplified Q-table is generated and used to optimize the complexity of the control algorithm. The results of simulation experiments show that our proposed control algorithm effectively improves the capacity of isolated intersections while reducing the time and space costs of the signal control algorithm.

scholarly journals Archaeological Survey Data Quality, Durability, and Use in the United States

2016 ◽  
Vol 4 (2) ◽  
pp. 106-117 ◽  
Author(s):  
Richard H. Wilshusen ◽  
Michael Heilen ◽  
Wade Catts ◽  
Karyn de Dufour ◽  
Bradford Jones
Keyword(s):  
Data Quality ◽  
Survey Data ◽  
Professional Training ◽  
New Technologies ◽  
Task Force ◽  
Field Work ◽  
The United States ◽  
Digital Data ◽  
Quality Data ◽  
Archaeological Survey

AbstractHigh-quality archaeological surveys and data are vital to preservation planning and mitigation efforts. Federal and state historic preservation offices (SHPOs) are accumulating and reviewing more data at an ever-faster pace. Given the critical nature of this information, a SAA task force was charged with assessing current survey practices and concerns. Our review indicates that survey policies and archaeological standards have improved substantially over the last two decades, but SHPOs remain challenged by insufficient professional training for field archaeologists, the need for standardization and integration of new technologies in field work, reporting, and review, as well as the sheer quantity and variety of digital data. A number of analytical tools and metrics are available to assess data quality, but seemingly there is not time or money for states to evaluate how to improve existing and future survey data. We draw upon a survey of SHPOs, a review of current literature, and our own experience to assess archaeological survey quality, data utility and durability for current and anticipated future uses. We offer suggestions on how to move forward, including consideration of an e-106 system for streamlining transfer and exchange of digital data and upgrading current approaches to survey and planning.

A new method for determination of time-of-day breakpoints based on clustering and image segmentation

2020 ◽  
Vol 47 (8) ◽  
pp. 974-981
Author(s):  
Hui Shen ◽  
Jing Yan ◽  
Daoguang Liu ◽  
Zhigui Liu
Keyword(s):  
Traffic Flow ◽  
Fixed Time ◽  
Time Of Day ◽  
Signal Timing ◽  
Time Control ◽  
Fuzzy C Means Clustering ◽  
Time Division ◽  
Overlap Rate ◽  
The City

Signal control is an important part of the transportation system and plays an important role in improving the capacity of intersections. This paper proposes a new traffic time division method for multiperiod fixed-time control strategy. Firstly, we put forward a new concept — transportation overlap rate — to complete the clustering of daily traffic flow patterns. Then, all the daily traffic flow data belonging to the same category are composed into a matrix, which is converted into the corresponding image later with the aim of using the fast and robust fuzzy C-means clustering (FRFCM) method to segment it. Finally, the traffic time division and breakpoint location are obtained through further analysis and processing of the segmentation results. For each period, the optimal signal cycle and green split are separately calculated by Webster’s signal timing method to satisfy different traffic demands of each period and effectively improve the operation efficiency of the intersection. The simulation results at a certain intersection in the city of Mianyang demonstrate the effectiveness and practicability of the proposed method.

Effect of Arterial Signalization and Level of Service on Measured Vehicle Emissions

2003 ◽  
Vol 1842 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Alper Unal ◽  
Nagui M. Rouphail ◽  
H. Christopher Frey
Keyword(s):  
Vehicle Emissions ◽  
The United States ◽  
Congestion Management ◽  
Traffic Signal ◽  
Level Of Service ◽  
Signal Timing ◽  
Signal Coordination ◽  
Chapel Hill ◽  
Arterial Traffic ◽  
Traffic Signal Timing

The effect of arterial traffic signal timing and coordination on vehicle emissions is studied. Traffic signal timing improvement is one of the most common practices for congestion management in the United States. Although the benefits of improved signal timing for reduced fuel consumption are well documented, its effectiveness as a transportation control measure for emissions has not been clearly investigated. An empirical approach based on real-world, on-road vehicle emissions measurements was used. A total of 824 one-way runs representing 100 h and 2,020 vehicle miles of travel were conducted involving four drivers and eight gasoline-fueled light-duty vehicles on two signalized arterials in Cary, North Carolina: Walnut Street and Chapel Hill Road. Modal analyses of the data indicate that emissions rates were highest during acceleration and tend to decrease (in descending order) for cruise, deceleration, and idle. A modal approach is used to quantify the effect of arterial traffic signal timing and coordination on emissions. A key result is that signal coordination on Walnut Street yielded measurable improvements in arterial level of service and emissions reduction. For Chapel Hill Road, emissions were substantially lower under uncongested conditions [level of service (LOS) A/B] than under congested conditions (LOS D/E) for travel in the same direction at different times of day. Findings confirm the utility of signal coordination and congestion management as effective tools for controlling emissions.

scholarly journals A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 722 ◽  
Author(s):  
Jorge Zambrano-Martinez ◽  
Carlos Calafate ◽  
David Soler ◽  
Lenin-Guillermo Lemus-Zúñiga ◽  
Juan-Carlos Cano ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Traffic Management ◽  
Real Data ◽  
Prediction Equation ◽  
City Management ◽  
Traffic Demand ◽  
Traffic Conditions

Currently, one of the main challenges that large metropolitan areas must face is traffic congestion. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, environmental pollution. By properly analyzing traffic demand, it is possible to predict future traffic conditions, using this information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution, thereby improving the traffic flow in a city in a fully centralized manner. This paper represents a step forward towards this novel traffic management paradigm by proposing a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. We perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed traffic prediction equation, combined with frequent updating of traffic conditions on the route server, can achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.

scholarly journals Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 137 ◽  
Author(s):  
Daeho Kim ◽  
Okran Jeong
Keyword(s):  
Reinforcement Learning ◽  
Traffic Flow ◽  
Learning Algorithm ◽  
Traffic Signal ◽  
Traffic Information ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Flow Prediction ◽  
Traffic Conditions ◽  
Flow Prediction

As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.

scholarly journals Traffic flow cellular automaton model with bi-directional information in an open boundary condition

2020 ◽  
Author(s):  
Liang Zheng ◽  
Zhengpeng He
Keyword(s):  
Traffic Flow ◽  
Traffic Information ◽  
Open Boundary ◽  
Hysteresis Phenomenon ◽  
Connected Vehicle ◽  
Directional Information ◽  
Congested Traffic ◽  
Traffic Conditions ◽  
Ca Model ◽  
Vehicle Technologies

Abstract With Connected Vehicle Technologies being popular, drivers not only perceive downstream traffic information but also get upstream information by routinely checking backward traffic conditions, and the backward-looking frequency or probability is usually affected by prevailing traffic conditions. Meanwhile, the bi-directional perception range of drivers is expected to significantly increase, which results in more informed and coordinated driving behaviours. So, we propose a traffic flow bi-directional CA model with two perception ranges, and perform the numerical simulations with the field data collected from a one-lane highway in Richmond, California, USA as the benchmark data. Numerical results show that the CA model can effectively reproduce the oscillation of relatively congested traffic and the traffic hysteresis phenomenon. When adjusting the backward-looking probability and the perception range, the CA model can well simulate the travel times of all vehicles, and the generation and dissolution of traffic jams under various scenarios.

Research on the Traffic Control Optimization at Weigong Village Intersection in Beijing

2014 ◽  
Vol 644-650 ◽  
pp. 2619-2622
Author(s):  
Zi Qing Li ◽  
Rui Song
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Field Survey ◽  
Service Level ◽  
Signal Timing ◽  
Related Data ◽  
Traffic Conditions ◽  
Control Optimization ◽  
Traffic Capacity ◽  
Control Scheme

The traffic flow, existing signal timing, lane arrangement and other field survey data were analyzed and computed in the paper first. Next, the traffic capacity and service level of Weigong village intersection were evaluated. Finally, Using Webster method to calculate the related data of signal timing, and Vissim are used to simulate the traffic conditions of the intersection. The result of simulation indicated that the optimized traffic control scheme reached the goal improving the operational condition of Weigong village intersection and unobstructed degree of road.

scholarly journals Estimating Signal Timing of Actuated Signal Control Using Pattern Recognition under Connected Vehicle Environment

2021 ◽  
Vol 33 (1) ◽  
pp. 153-163
Author(s):  
Ruochen Hao ◽  
Ling Wang ◽  
Wanjing Ma ◽  
Chunhui Yu
Keyword(s):  
Pattern Recognition ◽  
Traffic Flow ◽  
Quantitative Description ◽  
Estimation Method ◽  
Average Error ◽  
Signal Control ◽  
Signal Timing ◽  
Control Logic ◽  
Traffic Demand ◽  
Actuated Signal

The Signal Phase and Timing (SPaT) message is an important input for research and applications of Connected Vehicles (CVs). However, the actuated signal controllers are not able to directly give the SPaT information since the SPaT is influenced by both signal control logic and real-time traffic demand. This study elaborates an estimation method which is proposed according to the idea that an actuated signal controller would provide similar signal timing for similar traffic states. Thus, the quantitative description of traffic states is important. The traffic flow at each approaching lane has been compared to fluids. The state of fluids can be indicated by state parameters, e.g. speed or height, and its energy, which includes kinetic energy and potential energy. Similar to the fluids, this paper has proposed an energy model for traffic flow, and it has also added the queue length as an additional state parameter. Based on that, the traffic state of intersections can be descripted. Then, a pattern recognition algorithm was developed to identify the most similar historical states and also their corresponding SPaTs, whose average is the estimated SPaT of this second. The result shows that the average error is 3.1 seconds.

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