Analysis of GPS Based Vehicle Trajectory Data for Road Traffic Congestion Learning

Lanka, Swathi; Jena, Sanjay Kumar

doi:10.1007/978-3-319-07350-7_2

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The improvements are accomplished by appending preprocessing steps that neglect short-tracked sequences. The work in (Lanka et al, 2014) rectifies diverse traffic-related issues based on deep learning neural network that exploits the GPS trajectory information of floating cars in congestion prediction. Consequently, the work in (Chen et al, 2011) identifies the most popular traveling routes among the two locations.…”

Section: Literature Surveymentioning

confidence: 99%

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

Kamble

Kounte

2022

Transport and Telecommunication Journal

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Predicting the most favorable traveling routes for Vehicles plays an influential role in Intelligent Transportation Systems (ITS). Shortest Traveling Routes with high congestion grievously affect the driving comfort level of VANET users in populated cities. As a result, increase in journey time and traveling cost. Predicting the most favorable traveling routes with less congestion is imperative to minimize the driving inconveniences. A major downside of existing traveling route prediction models is to continuously learn the real-time road congestion data with static benchmarking datasets. However, learning the new information with already learned data is a cumbersome task. The main idea of this paper is to utilize incremental learning on the Hybrid Learning-based traffic Congestion and Timing Prediction (HL-CTP) to select realistic, congestion-free, and shortest traveling routes for the vehicles. The proposed HL-CTP model is decomposed into three steps: dataset construction, incremental and hybrid prediction model, and route selection. Firstly, the HL-CTP constructs a novel Traffic and Timing Dataset (TTD) using historical traffic congestion information. The incremental learning method updates the novel real-time data continuously with the TDD during prediction to optimize the performance efficiency of the hybrid prediction model closer to real-time. Secondly, the hybrid prediction model with various deep learning models performs better by taking the route prediction decision based on the best sub-predictor results. Finally, the HL-CTP selects the most favorable vehicle routes selected using traffic congestion, timing, and uncertain environmental information and enhances the comfort level of VANET users. In the simulation, the proposed HL-CTP demonstrates superior performance in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

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Section: Literature Surveymentioning

confidence: 99%

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

Kamble

Kounte

2022

Transport and Telecommunication Journal

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“…Traffic analysis is a thoroughly investigated field which is highly applicable in many fields of study including economic analysis, selecting and implementing traffic controls and evaluating the service quality of transport facilities [10]. The studies of traffic analysis and human mobility analysis rely on many forms of data including social network data [11], mobile human mobility data [12] and traffic sensor data [13,14].…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making

Bandaragoda

Adikari

Nawaratne

et al. 2020

Neural Comput & Applic

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Road traffic environments are highly dynamic and volatile with a multitude of roadside and external environmental factors contributing to its dynamicity. Apart from infrastructure-related means such as traffic lights, planned and unplanned road events and different road networks, a core component which contributes towards the traffic environment is the human factor which is heavily overlooked in the current studies. Due to diverse travel patterns of day-to-day activities, the commuter behaviour is directly depicted in traffic patterns providing an opportunity to further explore human behaviours using road traffic. Conducting such analysis would reveal different commuter behavioural patterns that can be used for optimization and timely management of operations. However, to conduct such real-time behaviour analysis, large volumes of high-frequency data are required with high granularity, as well as, a suitable technology to manage such data. Addressing these needs, we propose an environment-driven commuter behavioural model that can be used to elucidate diverse behaviours in road traffic environments. We conceptualized, designed and developed an artificial intelligence based commuter behaviour profiling framework to detect diverse commuter behavioural profiles, fluctuating and routine patterns among commuters using traffic flow profiling and travel trajectory analysis. We evaluated the framework using 190 million data points captured from the Bluetooth sensor network of the Melbourne arterial road network, in the state of Victoria in Australia. The results demonstrate that traffic flow profiling of the proposed framework can provide insights on recurrent commuter behaviours that are distinct to a selected area with a high granularity. Moreover, traffic trajectory analysis provides insights on non-recurrent behaviours such as accidents with regard to how such incidents impact the dynamics of the network and how the impact is propagated through the network. Besides road traffic management, the proposed framework will enable real-time decision-making when planning road infrastructure and support decision-making of government and business entities to optimize operations. KeywordsTraffic analysis Á Flow profiling Á Trajectory analysis Á Bluetooth data Á Human behaviour Á IoT & Naveen Chilamkurti

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“…Although GPS devices are becoming more and more used and affordable, only a limited number of vehicles are equipped with these devices, typically fleet management services (e.g., taxi, bus, truck, etc.) [7], especially in developing countries such as Vietnam, where public transport and commercial smart vehicles are not well-developed. Moreover, the traffic flow is a typical mixed traffic flow with predominantly motorcycles (over 80% of trips in the urban area by motorcycles [8]) without GPS devices.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

et al. 2022

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Traffic speed forecasting in the short term is one of the most critical parts of any intelligent transportation system (ITS). Accurate speed forecasting can support travelers’ route choices, traffic guidance, and traffic control. This study proposes a deep learning approach using long short-term memory (LSTM) network with tuning hyper-parameters to forecast short-term traffic speed on an arterial parallel multi-lane road in a developing country such as Vietnam. The challenge of mishandling the location data of vehicles on small and adjacent multi-lane roads will be addressed in this study. To test the accuracy of the proposed forecasting model, its application is illustrated using historical voyage GPS-monitored data on the Le Hong Phong urban arterial road in Haiphong city of Vietnam. The results indicate that in comparison with other models (e.g., traditional models and convolutional neural network), the best performance in terms of root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MDAE) is obtained by using the proposed model.

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Analysis of GPS Based Vehicle Trajectory Data for Road Traffic Congestion Learning

Cited by 6 publications

References 12 publications

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

Artificial intelligence based commuter behaviour profiling framework using Internet of things for real-time decision-making

Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach

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