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).

scholarly journals Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yuwei Bie ◽  
Mudasser Seraj ◽  
Can Zhang ◽  
Tony Z. Qiu
Keyword(s):  
Prediction Model ◽  
Prediction Error ◽  
Supply And Demand ◽  
Transmission Model ◽  
Speed Limit ◽  
Cell Transmission Model ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic State ◽  
State Prediction

Variable speed limit (VSL) is becoming recognized as an effective way to improve traffic throughput and road safety. In particular, methods based on traffic state prediction exhibit promising potential to prevent future traffic congestion and collisions. However, field observations indicate that the traffic state prediction model results in nonnegligible error that impacts the next step decision making of VSL. Thus, this paper investigates how to eliminate this prediction error within a VSL environment. In this study, the traffic state prediction model is a second-order traffic flow model named METANET, while the VSL control is model predictive control (MPC) based, and the VSL decision is discrete optimized choice. A simplified version of the switching mode stochastic cell transmission model (SCTM) is integrated with the METANET model to eliminate the prediction error. The performance of the proposed method is assessed using field data from a VSL pilot test in Edmonton, Canada, and is compared with the prediction results of the baseline METANET model during the road test. The results show that during the most congested period the proposed SCTM-METANET model significantly improves the prediction accuracy of regular METANET model.

scholarly journals Traffic speed prediction using ensemble kalman filter and differential evolution

2019 ◽  
Vol 259 ◽  
pp. 02001
Author(s):  
Lukáš Rapant
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Intelligent Transportation System ◽  
Transmission Model ◽  
Traffic Data ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Machine Learning Approach ◽  
Speed Prediction ◽  
Traffic Speed

Importance of traffic state prediction steadily increases with growing volume of traffic. Ability to predict traffic speed in short to medium horizon (i.e. up to one hour) is one of the main tasks of every newly developed Intelligent Transportation System. There are two possible approaches to this prediction. The first is to utilize physical properties of the traffic flow to construct an exact or approximate numerical model. This approach is, however, almost impossible to implement on a larger scale given the difficulty to obtain enough traffic data to describe the starting and boundary conditions of the model. The other option is to use historical traffic data and relate information and patterns they contain to the current traffic state by application of some form of statistical or machine learning approach. We propose to use combination of Ensemble Kalman filter and Cell Transmission Model for this task. These models combine properties of physical model with ability to incorporate uncertainty of the traffic data.

scholarly journals Lane-Based Traffic Signal Simulation and Optimization for Preventing Overflow

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1368
Author(s):  
Chi-kwong Wong ◽  
Yiu-yin Lee
Keyword(s):  
Traffic Signal ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Design Constraint ◽  
Traffic Demand ◽  
Cycle Times ◽  
Simulation And Optimization ◽  
The Road ◽  
Proposed Model ◽  
Lane Based

In the lane-based signal optimization model, permitted turn directions in the form of lane markings that guide road users to turn at an intersection are optimized with traffic signal settings. The spatial queue requirements of approach lanes should be considered to avoid the overdesigning of the cycle, effective red, and effective green durations. The point-queue system employed in the conventional modeling approach is unrealistic in many practical situations. Overflow conditions cannot be modeled accurately, while vehicle queues are accumulated that block back upstream intersections. In a previous study, a method was developed to manually refine the traffic signal settings by using the results of lane-based optimization. However, the method was inefficient. In the present study, new design constraint sets are proposed to control the effective red and effective green durations, such that traffic enters the road lanes without overflow. The reduced cycle times discharge the accumulated vehicles more frequently. Moreover, queue spillback and residual queues can be avoided. One of the most complicated four-arm intersections in Hong Kong is considered as a case study for demonstration. The existing traffic signal settings are ineffective for controlling the observed traffic demand, and overflow occurs in short lanes. The optimized traffic signal settings applied to the proposed optimization algorithm effectively avoided traffic overflow. The resultant queuing dynamics are simulated using TRANSYT 15 Cell Transmission Model (CTM) to verify the proposed model. The model application is extended to handle the difficult residual queue scenario. It is found that the proposed model can optimize the traffic signal settings in cases where there are short initial residual queues.

scholarly journals Research on Analysis Method of Traffic Congestion Mechanism Based on Improved Cell Transmission Model

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Hongzhao Dong ◽  
Shuai Ma ◽  
Mingfei Guo ◽  
Dongxu Liu
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Network ◽  
Simulation Experiment ◽  
Transmission Model ◽  
Spatial Diffusion ◽  
Cell Transmission Model ◽  
Analysis Method ◽  
Proposed Model ◽  
Cell Transmission

To analyze the spreading regularity of the initial traffic congestion, the improved cell transmission model (CTM) is proposed to describe the evolution mechanism of traffic congestion in regional road grid. Ordinary cells and oriented cells are applied to render the crowd roads and their adjacent roads. Therefore the traffic flow could be simulated by these cells. Resorting to the proposed model, the duration of the initial traffic congestion could be predicted and the subsequent secondary congestion could be located. Accordingly, the spatial diffusion of traffic congestion could be estimated. At last, taking a road network region of Hangzhou city as an example, the simulation experiment is implemented to verify the proposed method by PARAMICS software. The result shows that the method could predict the duration of the initial congestion and estimate its spatial diffusion accurately.

scholarly journals Calibration Framework based on Bluetooth Sensors for Traffic State Estimation Using a Velocity based Cell Transmission Model

2014 ◽  
Vol 3 ◽  
pp. 972-981 ◽  
Author(s):  
Andreas Allström ◽  
Alexandre M. Bayen ◽  
Magnus Fransson ◽  
David Gundlegård ◽  
Anthony D. Patire ◽  
...  
Keyword(s):  
State Estimation ◽  
Transmission Model ◽  
Cell Transmission Model ◽  
Traffic State ◽  
Traffic State Estimation ◽  
Cell Transmission

scholarly journals Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On-Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Mehrab Sayadi ◽  
Najaf Zare ◽  
Armin Attar ◽  
Seyyed Mohammad Taghi Ayatollahi
Keyword(s):  
Blood Pressure ◽  
Parameter Estimation ◽  
Cardiovascular Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Dynamic Prediction ◽  
Proposed Model ◽  
Significant Difference ◽  
Over Time ◽  
Deviance Statistic

Landmark model (LM) is a dynamic prediction model that uses a longitudinal biomarker in time-to-event data to make prognosis prediction. This study was designed to improve this model and to apply it to assess the cardiovascular risk in on-treatment blood pressure patients. A frailty parameter was used in LM, landmark frailty model (LFM), to account the frailty of the patients and measure the correlation between different landmarks. The proposed model was compared with LM in different scenarios respecting data missing status, sample size (100, 200, and 400), landmarks (6, 12, 24, and 48), and failure percentage (30, 50, and 100%). Bias of parameter estimation and mean square error as well as deviance statistic between models were compared. Additionally, discrimination and calibration capability as the goodness of fit of the model were evaluated using dynamic concordance index (DCI), dynamic prediction error (DPE), and dynamic relative prediction error (DRPE). The proposed model was performed on blood pressure data, obtained from systolic blood pressure intervention trial (SPRINT), in order to calculate the cardiovascular risk. Dynpred, coxme, and coxphw packages in the R.3.4.3 software were used. It was proved that our proposed model, LFM, had a better performance than LM. Parameter estimation in LFM was closer to true values in comparison to that in LM. Deviance statistic showed that there was a statistically significant difference between the two models. In the landmark numbers 6, 12, and 24, the LFM had a higher DCI over time and the three landmarks showed better performance in discrimination. Both DPE and DRPE in LFM were lower in comparison to those in LM over time. It was indicated that LFM had better calibration in comparison to its peer. Moreover, real data showed that the structure of prognostic process was predicted better in LFM than in LM. Accordingly, it is recommended to use the LFM model for assessing cardiovascular risk due to its better performance.

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