scholarly journals Macroscopic Lane Change Model—A Flexible Event-Tree-Based Approach for the Prediction of Lane Change on Freeway Traffic

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 864-880
Author(s):  
Christina Ng ◽  
Susilawati Susilawati ◽  
Md Abdus Samad Kamal ◽  
Irene Chew Mei Leng
Keyword(s):  
Cell Size ◽  
Binary Logistic Regression ◽  
Relaxation Method ◽  
Transmission Model ◽  
Lane Change ◽  
Event Tree ◽  
Cell Transmission Model ◽  
Trajectory Data ◽  
Prediction Ability ◽  
Freeway Traffic

Binary logistic regression has been used to estimate the probability of lane change (LC) in the Cell Transmission Model (CTM). These models remain rigid, as the flexibility to predict LC for different cell size configurations has not been accounted for. This paper introduces a relaxation method to refine the conventional binary logistic LC model using an event-tree approach. The LC probability for increasing cell size and cell length was estimated by expanding the LC probability of a pre-defined model generated from different configurations of speed and density differences. The reliability of the proposed models has been validated with NGSIM trajectory data. The results showed that the models could accurately estimate the probability of LC with a slight difference between the actual LC and predicted LC (95% Confidence Interval). Furthermore, a comparison of prediction performance between the proposed model and the actual observations has verified the model’s prediction ability with an accuracy of 0.69 and Area Under Curve (AUC) value above 0.6. The proposed method was able to accommodate the presence of multiple LCs when cell size changes. This is worthwhile to explore the importance of such consequences in affecting the performance of LC prediction in the CTM model.

scholarly journals Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Christina Ng ◽  
Susilawati Susilawati ◽  
Md Abdus Samad Kamal ◽  
Irene Mei Leng Chew
Keyword(s):  
Prediction Model ◽  
Transmission Model ◽  
Lane Change ◽  
Traffic Data ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Proposed Model ◽  
Significant Difference ◽  
The Mean ◽  
Change Prediction

This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).

Analysis of an Adaptive Iterative Learning Algorithm for Freeway Ramp Flow Imputation

2011 ◽  
Author(s):  
Ajith Muralidharan ◽  
Roberto Horowitz
Keyword(s):  
Learning Algorithm ◽  
A Priori ◽  
Iterative Learning ◽  
Equivalent Model ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Effective Demand ◽  
Freeway Traffic ◽  
Flow Profiles ◽  
Cell Transmission

We present an adaptive iterative learning based flow imputation algorithm, to estimate missing flow profiles in on ramps and off ramps using a freeway traffic flow model. We use the Link-Node Cell transmission model to describe the traffic state evolution in freeways, with on ramp demand profiles and off ramp split ratios (which are derived from flows) as inputs. The model based imputation algorithm estimates the missing flow profiles that match observed freeway mainline detector data. It is carried out in two steps: (1) adaptive iterative learning of an “effective demand” parameter, which is a function of ramp demands and off ramp flows/ split ratios; (2) estimation of on ramp demands/ off ramp split ratios from the effective demand profile using a linear program. This paper concentrates on the design and analysis of the adaptive iterative learning algorithm. The adaptive iterative learning algorithm is based on a multi-mode (piecewise non-linear) equivalent model of the Link-Node Cell transmission model. The parameter learning update procedure is decentralized, with different update equations depending on the local a-priori state estimate and demand estimate. We present a detailed convergence analysis of our approach and finally demonstrate some examples of its application.

Lane Change Rates at Freeway Weaving Sites: Trends in HCM6 and from NGSIM Trajectories

2019 ◽  
Vol 2673 (5) ◽  
pp. 627-636 ◽  
Author(s):  
Ishtiak Ahmed ◽  
Dezhong Xu ◽  
Nagui Rouphail ◽  
Alan Karr
Keyword(s):  
Empirical Data ◽  
Segment Length ◽  
Level Of Service ◽  
Lane Change ◽  
Travel Times ◽  
Trajectory Data ◽  
Freeway Traffic ◽  
Lane Changes

Concerns have been raised about the HCM6 weaving method’s lack of sensitivity to weaving segment length. This study explores the trends in HCM6 as they relate to lane change estimates and their impact on the segment speed and level of service (LOS). The study also compares HCM6 estimates of lane changes against empirical data from an NGSIM weaving site. Thus, the objectives of this study are twofold: ( a) critically investigate the effect of weaving length on lane change and associated speed model estimates in HCM6, and ( b) analyze trends in lane changes against congestion levels using detailed NGSIM trajectory data, comparing against HCM6 estimates. For ( a) it was found that the lack of sensitivity to weave length is because of the absence of this parameter in the nonweaving lane change and speed models. For ( b), a comparison of HCM6 lane change rates with NGSIM, US-101 data confirmed that the HCM6 estimates for weaving vehicles are fully consistent with those at the NGSIM site, controlling for density. In contrast, nonweaving lane change estimates in HCM6 did not deliver the expected trends, with more discretionary lane changes predicted as congestion increased. Finally, analysis of lane change patterns at the NGSIM site revealed a tendency for early merging for freeway to ramp traffic and uniform merging for ramp to freeway traffic over the length of the weave. Interestingly, a speed analysis showed that in most cases, a higher frequency of discretionary lane changes yielded lower travel times for drivers executing them.

Freeway Ramp Metering Based on Cell Transmission Model and Single Neuron

2013 ◽  
Vol 423-426 ◽  
pp. 2877-2881
Author(s):  
Yan Di Ye ◽  
Yan Yan Liu ◽  
Xin Rong Liang ◽  
Chao Jun Dong
Keyword(s):  
Traffic Congestion ◽  
Single Neuron ◽  
Learning Algorithm ◽  
Ramp Metering ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Freeway Traffic ◽  
Tracking Process ◽  
Cell Transmission ◽  
Control Objective

In this work, we apply single neuron method to relieve freeway traffic congestion. We consider a freeway composed of cells and entry/exit ramps, and formulate the ramp metering problem as a density tracking process. The cell transmission model (CTM) is firstly formulated and ramp control objective is determined. Based on CTM and single neuron, a freeway ramp metering system is then designed, and the learning algorithm of single neuron is given in detail. Finally, the ramp metering system is simulated in MATLAB software. The results show that this method can effectively deal with this class of control problem, and can achieve a perfect density tracking performance. This ramp metering can eliminate traffic congestion and maintain traffic flow stability.

Variable speed limit strategies’ analysis with cell transmission model on freeway

2017 ◽  
Vol 31 (24) ◽  
pp. 1750219 ◽  
Author(s):  
Shubin Li ◽  
Danni Cao
Keyword(s):  
Traffic Flow ◽  
Dynamic Performance ◽  
Transmission Model ◽  
Speed Limit ◽  
Cell Transmission Model ◽  
Variable Speed ◽  
Primary Methods ◽  
Variable Speed Limit ◽  
Freeway Traffic ◽  
Cell Transmission

Mainline freeway traffic flow control is one of the primary methods of traffic management, which can present the best network situation. In this paper, we integrate variable speed limit (VSL) strategy into the cell transmission model (CTM). Then the implementation of the integrated model on freeway traffic network is discussed. A novel optimal model of controlling freeway traffic flow is proposed for minimizing the total travelling time in the network. A solution algorithm is designed by using a simulation method. Considering the main purpose of the speed limit strategy is to control the mainstream flow, we compare the case where the VSL is used with the one without VSL. A simulation is implemented to show that the control strategy is efficient in describing system’s dynamic performance and the dynamic speed limit strategy significantly alleviates congestion.

scholarly journals The Study of Location Specific Lane Change Impacts on Traffic Delay using Extended Cell Transmission Model

2018 ◽  
Vol 203 ◽  
pp. 05008
Author(s):  
Susilawati Susilawati ◽  
Kar Yow Tan ◽  
Kamal Md Abdus Samad ◽  
Christina Ng
Keyword(s):  
Flow Speed ◽  
Transmission Model ◽  
Lane Change ◽  
Cell Transmission Model ◽  
Time Step ◽  
Lane Changing ◽  
High Tendency ◽  
Change Rate ◽  
Cell Transmission ◽  
Real Traffic

This paper comprehensively evaluates the influence of location specific lane change executions on delay and queue length. The extended macroscopic Cell Transmission Model (CTM) based lane change model has been developed by adopting the formulations of diverging and merging in one-way traffic flow. The extended CTM model uses a pre-determined lane change rate and traffic parameters including free flow speed, jam density, cell length and time-step while the inflow parameters were generated form real traffic data collected in Bandar Sunway, Malaysia. The results demonstrate that the lane change maneuver approaching the exit of intersection incurs the highest delay. From real observation, drivers have high tendency to perform mandatory lane changing compared with discretionary lane change in urban arterials. The results can be further used to develop lane change assistance model to improve the traffic flows.

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