Development of road traffic control by using integrated VISSIM-MATLAB simulation environment

Transportation Research Part C: Emerging Technologies

Dai

et al. 2018

This article proposes a macroscopic traffic control strategy to reduce fuel consumption of vehicles on highways. By implementing Greenshields fundamental diagram, the solution to Moskowitz equations is expressed as linear functions with respect to vehicle inflow and outflow, which leads to generation of a linear traffic flow model. In addition, we build a quadratic cost function in terms of vehicle volume to estimate fuel consumption rate based on COPERT model. A convex quadratic optimization problem is then formulated to generate energy-efficient traffic control decisions in real-time. Simulation results demonstrate significant reduction of fuel consumption on testing highway sections under peak traffic demands of busy hours. AbstractThis article proposes a macroscopic traffic control strategy to reduce fuel consumption of vehicles on highways. By implementing Greenshields fundamental diagram, the solution to Moskowitz equations is expressed as linear functions with respect to vehicle inflow and outflow, which leads to generation of a linear traffic flow model. In addition, we build a quadratic cost function in terms of vehicle volume to estimate fuel consumption rate based on COPERT model. A convex quadratic optimization problem is then formulated to generate energyefficient traffic control decisions in real-time. Simulation results demonstrate significant reduction of fuel consumption on testing highway sections under peak traffic demands of busy hours. based method in a MPC framework. However, although macroscopic traffic flow model, e.g. FASTLANE and METANET, have been adopted in energy-efficient traffic management, it is time consuming to find a convergent solution when a highly nonlinear traffic flow model is considered (Zegeye, 2011). Speed intervals have been used to obtain an approximate solution without solving highly 50 nonlinear dynamics, which results in accumulative errors over time (Dai et al., 2015).This work focuses on managing one type of highway infrastructure, dynamic speed limit signs, to control traffic flow speeds in order to reduce total fuel consumption during a specific time period. We adopt Lighthill-Whitham-Richard 55 (LWR) macroscopic traffic flow model, introduced by Ligthill and Whitham in the 1950's (Lighthill and Whitham, 1955), and COPERT fuel consumption esti-where J f and J t are fuel consumption estimated by COPERT and TTT of all vehicles, respectively. J f,norm and J t,norm denote two nominal values which are used for normalization. They can be obtained by estimating the fuel consumption and TTT in uncontrolled scenario. Weighting factor θ is introduced as an empirical parameter. The newly constructed multi-objective optimization 240 problem is still a CQOP and can be solved using the convex optimization solver

Section: Data From Real-world Scenarios and Vissim Setupmentioning

confidence: 99%

Real-time energy-efficient traffic control via convex optimization

Transportation Research Part C: Emerging Technologies

Dai

et al. 2018

“…The COM interface provides access to dynamically change simulation parameters of VISSIM in real time [34], [35]. To control speed limit in this study, the desired speed limits are dynamically adjusted at upstream point of each highway segment for every 2 mins.…”

Section: Implementation Of the Hybrid Traffic Control Strategymentioning

confidence: 99%

Hybrid Optimal Control for Time-Efficient Highway Traffic Management

2018 Annual American Control Conference (ACC)

Dai

et al. 2018

This article examines the hybrid traffic control problem to minimize total travel time (TTT) of a highway network through traffic management infrastructures, including dynamic speed limit signs, ramp metering, and information board.We first build the traffic flow model based on the Moskowitz function for each highway link to predict traffic status within a control horizon. The traffic density is predicted based on the flow dynamic model and corrected periodically by measured traffic flow data. The minimum TTT traffic control problem is then formulated as a mixed-integer quadratic programming problem with quadratic constraints. Numerical simulation of a real world highway network is provided to demonstrate significant reduction of TTT and alleviation of traffic congestion compared to results obtained from ALINEA and PI-ALINEA methods. Abstract-This article examines the hybrid traffic control problem to minimize total travel time (TTT) of a highway network through traffic management infrastructures, including dynamic speed limit signs, ramp metering, and information board.We first build the traffic flow model based on the Moskowitz function for each highway link to predict traffic status within a control horizon. The traffic density is predicted based on the flow dynamic model and corrected periodically by measured traffic flow data. The minimum TTT traffic control problem is then formulated as a mixed-integer quadratic programming problem with quadratic constraints. Numerical simulation of a real world highway network is provided to demonstrate significant reduction of TTT and alleviation of traffic congestion compared to results obtained from ALINEA and PI-ALINEA methods.

“…4. The performance of the proposed ANN model was evaluated by MSE that was programmed with MathWorks Matlab [19]. MSE was deduced as 16 second delay for 13 intervals of ACP recording points in S 1 .…”

Section: B Social Sensing Prca Event and Prediction Model Of Bus Arrmentioning

confidence: 99%

Social Sensing Enhanced Time Ruler for Real-Time Bus Service

2013 Ninth International Conference on Semantics, Knowledge and Grids

Gong

et al. 2013

Social sensing helps realize the real-time prediction of bus routes by perceiving social events and evaluating their influence on the time when a bus passes through a road section. This paper proposes a social sensing enhanced real-time bus service that integrates sensing ability and social networks to better understand and measure social events that influence vehicle velocity. The establishment of the real-time service revolves around the PT service quality attributions PEAs and the road condition attributions PRCAs. The processes that collect bus relevant events and further categorize them into PEA events or PRCA events were proposed respectively. A method of scoring PEAs for travelers according to social events was discussed. An artificial neural network based prediction model was proposed to estimate the bus arrival time by analyzing PRCA events.