scholarly journals Lane Change Control Combined with Ramp Metering: A Strategy to Manage Delays at On-Ramp Merging Sections

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hari Hara Sharan Nagalur Subraveti ◽  
Victor L. Knoop ◽  
Bart van Arem
Keyword(s):  
Travel Time ◽  
Control Strategy ◽  
Ramp Metering ◽  
Control Measures ◽  
Lane Change ◽  
Total Delay ◽  
Individual Control ◽  
Combined Control ◽  
Total Travel Time ◽  
The Individual

Control measures at merging locations aimed at either the mainline traffic or on-ramp traffic do not lead to a fairness in the distribution of total delay across the two streams. This paper presents a control strategy of combining a lane change control with a ramp metering system at motorway merges. The control strategy presents the opportunity to control the delays incurred at the two traffic streams of the merge. An optimization problem is formulated for a multilane motorway with an on-ramp with the aim to minimize the total travel time of the system. The proposed strategy is tested using an incentive-based lane-specific traffic flow model. Results revealed a 17% reduction in the total travel time due to the proposed strategy. Moreover, it was shown that the distribution of delays over the mainline and on-ramp could be controlled via the proposed strategy. The performance of the combined control was also compared to the individual control measures. It was observed that the individual control measures (lane change only and ramp metering only) lead to high delays on either the mainline or on-ramp compared to the combined control, where the balance between the delay for the drivers on the mainline and on-ramp could be regulated. The combined lane change and ramp metering control presents opportunities for the road authorities to manage the total delay distribution across the two traffic streams.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

Fuzzy Ramp Metering: Design Overview and Simulation Results

1998 ◽  
Vol 1634 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Cynthia Taylor ◽  
Deirdere Meldrum ◽  
Les Jacobson
Keyword(s):  
Fuzzy Logic ◽  
Travel Time ◽  
Fuzzy Controller ◽  
Simulation Software ◽  
Ramp Metering ◽  
Traffic Conditions ◽  
Total Distance ◽  
Total Travel Time ◽  
Vehicle Delay ◽  
Simulation Results

A fuzzy logic ramp-metering algorithm was designed to overcome the limitations of conventional ramp-metering strategies. The fuzzy controller demonstrated improved robustness, prevented heavy congestion, intelligently balanced conflicting needs, and tuned easily. The objective was to maximize total distance traveled and minimize total travel time and vehicle delay, while maintaining acceptable ramp queues. A multiple-ramp study site from the Seattle I-5 corridor was modeled and tested using the freeway simulation software, FRESIM. For five of the six testing sets, encompassing a variety of traffic conditions, the fuzzy controller outperformed the three other controllers tested.

A micro-simulation study on proactive coordinated ramp metering for relieving freeway congestion

2016 ◽  
Vol 43 (7) ◽  
pp. 599-608 ◽  
Author(s):  
Xu Wang ◽  
Tony Z. Qiu ◽  
Lei Niu ◽  
Ruhua Zhang ◽  
Lu Wang
Keyword(s):  
Prediction Model ◽  
Travel Time ◽  
Control Algorithm ◽  
Effective Field ◽  
Ramp Metering ◽  
Successful Implementation ◽  
Control Framework ◽  
Micro Simulation ◽  
Total Travel Time ◽  
Freeway Congestion

To relieve freeway congestion during peak periods, ramp metering (RM) is often implemented to control the input flow from onramps on freeways. Many studies focus on proactive coordinated RM controls; however, successful implementation of proactive RM control still requires a more accurate prediction model and a less complex control algorithm. To this end, this study tests a proactive RM approach in micro-simulation, with goals to improve network-wide travel time and traffic flow. A METANET-based dynamic traffic model was adopted as a prediction model within a predictive control framework. The evaluation revealed a 6.50% amelioration in total travel time on the mainline and a 2.52% reduction of total time spent in the network. The applied algorithm was compared with the HERO algorithm and implemented in various peak demand scenarios. This analysis could lead to efficient and effective field applications of proactive coordinated RM control to improve freeway operation.

scholarly journals Long-term evaluation of the operational performance of fixed time ramp metering control strategy: a freeway corridor study

2015 ◽  
Vol 42 (11) ◽  
pp. 910-918 ◽  
Author(s):  
Osama Osman ◽  
Julius Codjoe ◽  
Sherif Ishak ◽  
Jose Rodriguez ◽  
Marlene Russell
Keyword(s):  
Travel Time ◽  
Control Strategy ◽  
Traffic Control ◽  
Control Strategies ◽  
Fixed Time ◽  
Ramp Metering ◽  
Level Of Service ◽  
Traffic Conditions ◽  
Entry Points ◽  
Before And After

Ramp metering is one of the successful active traffic control strategies to control traffic flow at entry points to freeways. This study evaluates the effectiveness of fixed-time ramp metering control strategy on the day-to-day operation of traffic over two segments (easternmost and westernmost) of the I-12 corridor in Baton Rouge, Louisiana. Detector speeds and volumes were collected over 11 months and used to generate three performance measures, speed, travel time, and level of service to compare traffic conditions before and after the deployment of ramp meters. Comparative analysis, comprising statistical analysis, analysis of travel time savings, and level of service were then undertaken for traffic conditions before and after ramp meters installation. Overall, the results show some improvements in traffic conditions in the eastbound direction of the westernmost segment; however, the conditions slightly deteriorated on the westbound of the same segment. For the easternmost segment of I-12, no improvement was detected.

scholarly journals Reinforcement Learning Technique in Multiple Motorway Access Control Strategy Design

2012 ◽  
Vol 22 (2) ◽  
pp. 117-123 ◽  
Author(s):  
Kostandina Veljanovska ◽  
Kristi M. Bombol ◽  
Tomaž Maher
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Access Control ◽  
Travel Time ◽  
Control Strategy ◽  
Learning Algorithm ◽  
Traffic Flows ◽  
Traffic Demand ◽  
Q Learning ◽  
Total Travel Time

An appropriately designed motorway access control can decrease the total travel time spent in the system up to 30% and consequently increase the merging operations safety. To date, implemented traffic responsive motorway access control systems have been of local or regulatory type and not truly adaptive in the real sense of the meaning. Hence, traffic flow can be influenced positively by numerous intelligent transportation system (ITS) techniques. In this paper a contemporary approach is presented. It considers the design philosophy of an optimal and adaptive closed-loop multiple motorway access control strategy. The methodology proposed uses the artificial intelligence technique - known as reinforcement learning (RL) with multiple agents, and applies the Q-learning algorithm. One segment of the motorway network with three lanes in each direction and three motorway entries was designed. The detectors and traffic signals were placed at the entries (ramps). Traffic flows and traffic occupancy on the main line as well as the traffic demand on the motorway entries were taken as input model variables. The output variables referred to the travel speed on the corridor, the total travel time, and the total stop time. VISSIM micro-simulator and direct programming of the simulator functions were used in order to implement the RL technique. The peak hour was chosen for the time of simulation. The model was tested in two phases. Its effectiveness was compared to ALINEA. It was observed that the proposed strategy was capable of responding both to dynamic sensory inputs from the environment and to dynamically changing environment. The model of the environment and supervision were not required. The control policy changed as response to the inherent system characteristic changes. It was confirmed that the strategy was truly adaptive and real-time responsive to the traffic demand on the corridor. KEY WORDS: motorway access, traffic flows, control, strategy, artificial intelligence, Q-Learning, simulation

Modeling Network Travel Time Impact of Freeway Ramp Metering

1998 ◽  
Vol 1645 (1) ◽  
pp. 152-159
Author(s):  
Bruce N. Janson
Keyword(s):  
Travel Time ◽  
Dynamic Traffic Assignment ◽  
Ramp Metering ◽  
Time Varying ◽  
Assignment Model ◽  
Travel Time Savings ◽  
Total Travel Time ◽  
Alternative Routes ◽  
The Impact ◽  
Varying Demand

Whether freeway ramp metering can reduce total travel time in a corridor of several alternative routes depends on changes in route volumes and travel times. Ramp metering effectiveness and ramp metering algorithms have been evaluated mainly on the basis of improved freeway operations. Most studies have not evaluated the impact on alternative routes because of the complexity of the problem (e.g., which routes and what lengths of routes should be studied). An analysis of ramp metering impact in a network corridor is presented, first for simple steady-state cases and then for more complex cases involving time-varying demand, upstream and downstream queueing on freeway and alternative routes, and variable ramp metering rates based on freeway conditions. Time-varying examples are solved with a dynamic traffic assignment model called DYMOD. The analysis shows that ramp metering yields total travel time savings if (a) downstream freeway capacities are sufficiently restrictive, and (b) competitive alternative routes exist to accommodate the diverted traffic. The conditions under which ramp metering can be effective is illustrated by an examination of these simplified cases, and a useful modeling approach to analyzing systemwide impact in a larger corridor is demonstrated.

scholarly journals A Wind-Storage Combined Frequency Regulation Control Strategy Based on Improved Torque Limit Control

2021 ◽  
Vol 13 (7) ◽  
pp. 3765
Author(s):  
Benxi Hu ◽  
Fei Tang ◽  
Dichen Liu ◽  
Yu Li ◽  
Xiaoqing Wei
Keyword(s):  
Control Strategy ◽  
Storage System ◽  
Energy Storage System ◽  
Frequency Regulation ◽  
Small Scale ◽  
Acceleration Phase ◽  
Deceleration Phase ◽  
Combined Control ◽  
Inertial Response ◽  
Battery Energy

The doubly-fed induction generator (DFIG) uses the rotor’s kinetic energy to provide inertial response for the power system. On this basis, this paper proposes an improved torque limit control (ITLC) strategy for the purpose of exploiting the potential of DFIGs’ inertial response. It includes the deceleration phase and acceleration phase. To shorten the recovery time of the rotor speed and avoid the second frequency drop (SFD), a small-scale battery energy storage system (BESS) is utilized by the wind-storage combined control strategy. During the acceleration phase of DFIG, the BESS adaptively adjusts its output according to its state of charge (SOC) and the real-time output of the DFIG. The simulation results prove that the system frequency response can be significantly improved through ITLC and the wind-storage combined control under different wind speeds and different wind power penetration rates.

scholarly journals Intelligent Ramp Control for Incident Response Using Dyna-QArchitecture

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Chao Lu ◽  
Yanan Zhao ◽  
Jianwei Gong
Keyword(s):  
Reinforcement Learning ◽  
Travel Time ◽  
Single Agent ◽  
Superior Performance ◽  
Model Free ◽  
Road Users ◽  
Total Travel Time ◽  
The Uk ◽  
Traffic Operation ◽  
Ramp Control

Reinforcement learning (RL) has shown great potential for motorway ramp control, especially under the congestion caused by incidents. However, existing applications limited to single-agent tasks and based onQ-learning have inherent drawbacks for dealing with coordinated ramp control problems. For solving these problems, a Dyna-Qbased multiagent reinforcement learning (MARL) system named Dyna-MARL has been developed in this paper. Dyna-Qis an extension ofQ-learning, which combines model-free and model-based methods to obtain benefits from both sides. The performance of Dyna-MARL is tested in a simulated motorway segment in the UK with the real traffic data collected from AM peak hours. The test results compared with Isolated RL and noncontrolled situations show that Dyna-MARL can achieve a superior performance on improving the traffic operation with respect to increasing total throughput, reducing total travel time and CO2emission. Moreover, with a suitable coordination strategy, Dyna-MARL can maintain a highly equitable motorway system by balancing the travel time of road users from different on-ramps.

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