Estimating congestion zones and travel time indexes based on the floating car data

Erdelić, Tomislav; Carić, Tonči; Erdelić, Martina; Tišljarić, Leo; Turković, Ana; Jelušić, Niko

doi:10.1016/j.compenvurbsys.2021.101604

Cited by 39 publications

(25 citation statements)

References 54 publications

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“…Traffic patterns were defined by considering the Level of Service, which is based on segment speed on urban arterial roads. Attempts on predicting trajectories data for estimating traffic conditions from a large historical FCD are performed in [35]. The goal was twofold: on one hand the estimation of congestion zones on a large road network, on the other the estimation of travel times within congestion zones by the time-varying Travel Time Indexes (TTIs).…”

Section: Resultsmentioning

confidence: 99%

Vehicle trajectory prediction and generation using LSTM models and GANs

Rossi¹,

Ajmar²,

Paolanti³

et al. 2021

PLoS ONE

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Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.

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Section: Resultsmentioning

confidence: 99%

Vehicle trajectory prediction and generation using LSTM models and GANs

Rossi¹,

Ajmar²,

Paolanti³

et al. 2021

PLoS ONE

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“…Step 3 Snowballing A specific aspect of urban development that stood out was traffic control and transport-related applications (Finogeev et al, 2019). Embedded sensors in vehicles generate large amounts of floating car data (FCD: records of locations, and time-stamps registered during travel) and extended floating car data, which complements FCD with vehicular sensor data, often collected via on-board diagnosis interfaces (Erdelić et al, 2021;Voland & Asche, 2017). This can stimulate the development of smart vehicles able to transmit data for real-time analysis (Unal, Kocak, & Donmez, 2018) in large Internet of Vehicles applications (Zhong, Fang, & Zhao, 2013).…”

Section: Decisionmentioning

confidence: 99%

“…The intelligent analysis of IoT objects trajectories can be applied in many ways, such as traffic analysis and trip time estimation (Erdelić et al, 2021;Yi, Liu, Markovic, & Phillips, 2021), route prediction, and trajectory patterns learning (Sabek & Mokbel, 2019;Solomon, Livne, Katz, Shapira, & Rokach, 2021). All of this stimulates the creation of spatial data systems with APIs to support real-time ML operations in massive volumes of geospatial data (Sarwat, 2020) and frameworks and inference models for spatial ML (Sabek & Mokbel, 2019).…”

Section: Challenges and Research Directionsmentioning

confidence: 99%

Applications of geospatial big data in the Internet of Things

Silva

Holanda

2021

Transactions in GIS

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The Internet of Things (IoT) has emerged as a communication paradigm that allows the interconnection of devices that interact with the environment through sensors and actuators, collect data from it, and transmit these data over a network without requiring human interaction (Armstrong, Wang, & Zhang, 2019). IoT architectures can demand database management systems capable of handling high data generation and transmission rates, bringing the research on the IoT closer to the studies on big data (Ge,

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“…In this context, the usage of floating car data (FCD) can represent a solution for urban areas, since vehicle location, speed, and other information of vehicle trip can be collected in a dataset. Consequently, driving and parking habits of the users can be extracted from this type of dataset to reveal main patterns and how cars travel and park within different zones of a city [19,20]. However, most of the current literature limit the FCD usage to the traffic forecasting or the identification of potential congestion zones [19,21].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, driving and parking habits of the users can be extracted from this type of dataset to reveal main patterns and how cars travel and park within different zones of a city [19,20]. However, most of the current literature limit the FCD usage to the traffic forecasting or the identification of potential congestion zones [19,21]. Only a recent study figured out the potentiality of FCD investigating the environmental impact of different electric mobility scenarios in the urban area of Rome [22], but electricity demand of EV charging is not evaluated.…”

Section: Introductionmentioning

confidence: 99%

A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study

et al. 2021

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The development and the diffusion of the electromobility is crucial for reducing air pollution and increase sustainable transport. In particular, electrification of private mobility has a significantly role in the energy transition within urban areas, since the progressive substitution of conventional passenger cars by electric vehicles (EVs) leads to the decarbonisation of transport sector without direct emissions. However, increasing EV penetration in the market forces an expansion of the existing charging infrastructure with potential negative impacts on the distribution grid. In this context, a simplified approach is proposed to estimate the energy and power demand owing to the recharge of electric passenger cars within the city of Turin in Italy. This novel approach is based on the usage of floating car data (FCD) to identify the travel behaviour and parking habits of a non-EV passenger car in the city. Mobility data were then used to evaluate EVs energy consumption and charging needs considering different charging options (public or domestic) and range anxiety in different scenarios of EV diffusion. Aggregated load profiles and demand were finally evaluated both for the whole and for each zone of the city as possible resource for city planner or distribution system operators (DSO).

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Estimating congestion zones and travel time indexes based on the floating car data

Cited by 39 publications

References 54 publications

Vehicle trajectory prediction and generation using LSTM models and GANs

Vehicle trajectory prediction and generation using LSTM models and GANs

Applications of geospatial big data in the Internet of Things

A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study

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