Urban traffic flow prediction techniques: A review

Medina-Salgado, Boris; Sánchez-DelaCruz, Eddy; Pozos-Parra, Pilar; Sierra, Javier E.

doi:10.1016/j.suscom.2022.100739

Cited by 62 publications

(38 citation statements)

References 60 publications

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“…Figure 6 shows the prediction accuracy in traffic flow to improve the economy. Based on the dataset [27] acquired in the past from one or more observation points as derived in equation (5), it is possible to estimate the flow count at a future time using this information. As a result, traffic managers can take early action to regulate traffic load and avoid congestion.…”

Section: Dataset 4 Descriptionmentioning

confidence: 99%

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Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

2022

Mobile Information Systems

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Smart cities make better use of space and have less traffic, cleaner air, and more efficient municipal services, improving people’s quality of life. The vast number of vehicles continually seeking to reach crowded spots in smart cities complicates acquiring a public parking space. It presents challenges for both traffic and residents. With such vast populations, road congestion is a serious challenge. It wastes vital resources such as fuel, money, and, most importantly, time. Finding a good location to park is one of the reasons for traffic congestion on the highway. This paper proposes a deep learning-based economic forecasting model (DL-EFM) for long-term economic growth in smart cities. Traffic management is vital for cities to guarantee that people and products can move freely across the city. Many automobiles attempting to reach crowded areas in smart cities make getting a public parking place difficult. It is inconvenient for both drivers and residents. Different traffic management authorities have implemented an artificial neural network (ANN) to resolve the issue, and modern vehicle systems have been coupled with intelligent parking solutions. The experimental outcome of the deep learning-based economic forecasting model improves traffic estimation, accuracy prediction in traffic flow, traffic management, and smart parking when compared to existing methods.

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Section: Dataset 4 Descriptionmentioning

confidence: 99%

“…Travellers and vehicles will bene t from real-time parking information and tra c management [4]. Parking laws that encourage intelligent communities' development can enhance municipal governments' e ectiveness [5]. As urban areas increase now and in the future, smart parking will play a vital role in developing smart cities [6].…”

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confidence: 99%

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

2022

Mobile Information Systems

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“…Longterm projections are more likely than short-term forecasts to reduce travelers' average trip time [14]. Common forecasted traffic parameters include traffic flow [15], traffic speed [16], and traffic time [17].…”

Section: Discussionmentioning

confidence: 99%

“…Long-term projections are more likely than short-term forecasts to reduce travelers' average trip time [14]. Common forecasted traffic parameters include: traffic flow [15], traffic speed [16], and traffic time [17]. The increasing availability of large-scale traffic data, which can be looked at from a temporal and spatial lens, has paved the way to develop prediction models that are robust to capture the underlying driving mechanism of traffic volatilities, especially the random (unforeseen) components.…”

Section: Traffic Forecastingmentioning

confidence: 99%

“…The study concluded that by maintaining a 0.5-second gap between CVs, the capacity of the highway could be doubled under certain conditions. When vehicle communication was implemented in a four-lane highway merger scenario, van Arem et al [15] found that it had a statistically significant effect on traffic flow. The analysis discovered a slight improvement in traffic flow efficiency when vehicles were not equipped.…”

Section: Introductionmentioning

confidence: 99%

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Interactive, multi-purpose traffic prediction platform using connected vehicles dataset

Shoman¹

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Traffic congestion is a perennial issue because of the increasing traffic demand yet limited budget for maintaining current transportation infrastructure; let alone expanding them. Many congestion management techniques require timely and accurate traffic estimation and prediction. Examples of such techniques include incident management, real-time routing, and providing accurate trip information based on historical data. In this dissertation, a speech-powered traffic prediction platform is proposed, which deploys a new deep learning algorithm for traffic prediction using Connected Vehicles (CV) data. To speed-up traffic forecasting, a Graph Convolution -- Gated Recurrent Unit (GC-GRU) architecture is proposed and analysis of its performance on tabular data is compared to state-of-the-art models. GC-GRU's Mean Absolute Percentage Error (MAPE) was very close to Transformer (3.16 vs 3.12) while achieving the fastest inference time and a six-fold faster training time than Transformer, although Long-Short-Term Memory (LSTM) was the fastest in training. Such improved performance in traffic prediction with a shorter inference time and competitive training time allows the proposed architecture to better cater to real-time applications. This is the first study to demonstrate the advantage of using multiscale approach by combining CV data with conventional sources such as Waze and probe data. CV data was better at detecting short duration, Jam and stand-still incidents and detected them earlier as compared to probe. CV data excelled at detecting minor incidents with a 90 percent detection rate versus 20 percent for probes and detecting them 3 minutes faster. To process the big CV data faster, a new algorithm is proposed to extract the spatial and temporal features from the CSV files into a Multiscale Data Analysis (MDA). The algorithm also leverages Graphics Processing Unit (GPU) using the Nvidia Rapids framework and Dask parallel cluster in Python. The results show a seventy-fold speedup in the data Extract, Transform, Load (ETL) of the CV data for the State of Missouri of an entire day for all the unique CV journeys (reducing the processing time from about 48 hours to 25 minutes). The processed data is then fed into a customized UNet model that learns highlevel traffic features from network-level images to predict large-scale, multi-route, speed and volume of CVs. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets of the developed model and comparable benchmarks. To visually analyze the historical traffic data and the results of the prediction model, an interactive web application powered by speech queries is built to offer accurate and fast insights of traffic performance, and thus, allow for better positioning of traffic control strategies. The product of this dissertation can be seamlessly deployed by transportation authorities to understand and manage congestions in a timely manner.

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Anomalous vehicle recognition in smart cities using persistent bloom filter: Memory efficient and intelligent monitoring

Kumar,

Singh

2023

Trans Emerging Tel Tech

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Globally, there is a rapid and significant change in the distribution of the urban population. Concerning which there is an increase in traffic rule violations in smart cities, resulting in traffic congestion and accidents. Anomalous vehicle recognition in smart cities is a critical issue that demands prompt and real‐time responsive solutions to address such situations. This article proposes an effective technique utilizing a persistent bloom filter to store and retrieve vehicle data, to improve road safety in urban locations. The technique employs distinct bloom filters to store vehicle data based on the hour, day, month, and year, which enables effective temporal membership queries on vast data. The proposed approach of anomalous vehicle recognition in smart cities using a persistent bloom filter is a memory‐efficient and intelligent monitoring technique. According to experimental analysis on large datasets such as “Montgomery County 911 Calls", “EMS and Fire Calls", “Seattle Fire Dept: 911", and “Traffic Violations in Montgomery County", the approach has the potential to boost query efficiency by up to 80% in comparison to conventional methods, in addition to decreasing storage space demands by up to 60%. The proposed approach has the potential to bridge the gap between research and practice in the field of urban safety and to enhance road safety while considering the dynamic changes in the road network. Employing traditional approaches to detect violations and monitor real‐time changes in a vast dataset of vehicles can be a laborious undertaking. The proposed technique provides a reliable and efficient approach to handling big data and improving traffic safety in smart cities.

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Urban traffic flow prediction techniques: A review

Cited by 62 publications

References 60 publications

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

Feasibility of Economic Forecasting Model Based on Intelligent Algorithm of Smart City

Interactive, multi-purpose traffic prediction platform using connected vehicles dataset

Anomalous vehicle recognition in smart cities using persistent bloom filter: Memory efficient and intelligent monitoring

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