INTEGRATING MASTER PLANNING FOR URBAN DEVELOPMENT ON THE INTELLIGENT TRANSPORT SYSTEM IN VIETNAM

Purpose of the study: Intelligent Transport System (ITS) is being developed, researched, and built to solve traffic congestion in recent years. Many studies and seminars were organized but the results were still limited and fragmented. To build a smart city with intelligent transport system always starts from the planning work. Methodology: Methods of aggregating and statistical data from the Ministry of Transport to ITS and the overall planning of urban transport; Review and propose optimal solutions for integrated urban planning of ITS. Main Findings: The role of urban transportation and the current status of ITS development planning in urban planning in Vietnam. On that basis, it is necessary to integrate existing master plans into an urban development master plan that covers socio-economic development planning, land use, and urban construction. It is based on the planning of smart and modern urban transport development. Applications of this study: The paper proposes content to integrate ITS planning in urban planning in 4 steps to ensure sustainability and updates. Novelty/Originality of this study: The paper presents an overview of ITS system and ITS planning and its relation to urban planning in which, in addition to the traditional planning content, some new contents of ITS planning need to be integrated to meet for building and managing the city in a new age. This study is to affirm the need for integrating urban development master plans based on intelligent transportation system planning

Traffic Time Headway Prediction and Analysis: A Deep Learning Approach

In the modern world of Intelligent Transportation System (ITS), time headway is a key traffic flow parameter affecting ITS operations and planning. Defined as “the time difference between any two successive vehicles when they cross a given point”, time headway is used in various traffic and transportation engineering research domains, such as capacity analysis, safety studies, car-following, and lane-changing behavior modeling, and level of service evaluation describing stochastic features of traffic flow. Advanced travel and headway information can also help road users avoid traffic congestion through dynamic route planning, for instance. Hence, it is crucial to accurately model headway distribution patterns for the purpose of analyzing traffic operations and making subsequent infrastructure-related decisions. Previous studies have applied a variety of probabilistic models, machine learning algorithms (for example, support vector machine, relevance vector machine, etc.), and neural networks for short-term headway prediction. Recently, deep learning has become increasingly popular following a surge of traffic big data with high resolution, thriving algorithms, and evolved computational capacity. However, only a few studies have exploited this emerging technology for headway prediction applications. This is largely due to the difficulty in capturing the random, seasonal, nonlinear, and spatiotemporal correlated nature of traffic data and asymmetric human driving behavior which has a significant impact on headway. This study employs a novel architecture of deep neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamics effectively to predict vehicle headway. LSTM NN can overcome the issue of back-propagated error decay (that is, vanishing gradient problem) existing in regular Recurrent Neural Network (RNN) through memory blocks which is its special feature, and thus exhibits superior capability for time series prediction with long temporal dependency. There is no existing appropriate model for long term prediction of traffic headway, as existing models lack using big dataset and solving the vanishing gradient problem because of not having a memory block. To overcome these critics and fill the gaps in previous works, multiple LSTM layers are stacked to incorporate temporal information. For model training and validation, this study used the USDOT’s Next Generation Simulation (NGSIM) dataset, which contains historical data of some important features to describe the headway distribution such as lane numbers, microscopic traffic flow parameters, vehicle and road shape, vehicle type, and velocity. LSTM NN can capture the historical relationships between these variables and save them using its unique memory block. At the headway prediction stage, the related spatiotemporal features from the dataset (HighwayI-80) were fed into a fully connected layer and again tested with testing data for validation (both highway I-80 & US 101). The predicted accuracy outperforms previous time headway predictions

Short-Term Traffic Participants Behaviour Prediction for Automated Vehicles

Due to the rapid commencement of autonomous vehicles and the promising potential benefits, it has made it critical for said vehicles to be able to interpret their environment and compensate for the absence of driver predictions from visual cues. This study presents a novel intermediate component to improve the performance of autonomous vehicle controllers, by providing them with real-time microscopic predictions of traffic participants' behaviour, given the environmental conditions. This strategy is especially aimed towards direct combination with model-predictive controllers (MPCs) and other controllers that can utilize dynamic state predictions. This task is undertaken in three stages for three different scenarios. Scenario I considers V2X communications and predicts the velocity of an arbitrary vehicle in longitudinal direction. Using a recurrent neural network (RNN) and considering a complementary variable the strategy can predict the speed profile of said vehicle for arbitrary horizons. Results of this scenario exhibit >0.95 correlation if trained with enough data. Scenario II moves on to a more sophisticated approach for prediction of vehicles on US-101, using real data provided by the U.S. Federal Highway Administration (FHWA) under NGSIM. Utilizing a marriage of dynamic Bayesian network (DBN) and RNN, the method can make predictions on speed profiles of all present vehicles within a range, for arbitrary horizons, as well as prediction on whether the vehicle on the main lanes would yield to the merging vehicles on the ramp. Due to digital nature of the DBN stream, a Kalman filter (KF) was introduced as post processing smoothing method. Results of this scenario exhibit >0.95 correlation and <1.6 mph mean absolute error. Scenario III tackles a much more complex driving environment, intersection driving. Because in intersection driving, the priority relationships of highway driving are no longer existent, the training must be broadened to encompass vehicle pairs which is exponentially more difficult than training for single vehicles. The data for this phase was generated by SUMO. Results of this scenario exhibit <1.1 mph mean absolute error. Scenario IV focuses on the problem of roundabout driving. In roundabout driving, the general driving situation is more similar to highway merging, however due to the rapid move toward replacing intersections with roundabouts, especially in developing cities, definitely an important scenario to look at. In this scenario SUMO was used for data generation, a new DBN topology was developed and the results yielded exhibit >0.89 correlation. To evaluate the performance and the accuracy of the proposed method, it was compared with a collection of sequence prediction techniques, including LSTM and GRU. It was concluded that the DBN-RNN has the best accuracy and performance among these methods. Validation of the strategy was planned to be done on the scaled autonomous vehicle test platform developed in Smart Hybrid and Electric Vehicles Systems (SHEVS) lab, where driver-in-the-loop hardware was incorporated and the equipment were prepared but due to COVID-19 closures was not realized