scholarly journals Using Bluetooth Detectors to Monitor Urban Traffic Flow with Applications to Traffic Management

2021 ◽  
Author(s):  
◽  
Mohsen Hajsalehi Sichani
Keyword(s):  
Traffic Flow ◽  
Large Scale ◽  
Traffic Monitoring ◽  
Urban Traffic ◽  
Monitoring Systems ◽  
Traffic Modelling ◽  
Typical Day ◽  
Kaikoura Earthquake ◽  
Traffic Operation ◽  
The City

<p>A comprehensive traffic monitoring system can assist authorities in identifying parts of a road transportation network that exhibit poor performance. In addition to monitoring, it is essential to develop a localized and efficient analytical transportation model that reflects various network scenarios and conditions. A comprehensive transportation model must consider various components such as vehicles and their different mechanical characteristics, human and their diverse behaviours, urban layouts and structures, and communication and transportation infrastructure and their limitations. Development of such a system requires a bringing together of ideas, tools, and techniques from multiple overlapping disciplines such as traffic and computer engineers, statistics, urban planning, and behavioural modelling. In addition to modelling of the urban traffic for a typical day, development of a large-scale emergency evacuation modelling is a critical task for an urban area as this assists traffic operation teams and local authorities to identify the limitations of traffic infrastructure during an evacuation process through examining various parameters such as evacuation time. In an evacuation, there may be severe and unpredictable damage to the infrastructure of a city such as the loss of power, telecommunications and transportation links. Traffic modelling of a large-scale evacuation is more challenging than modelling the traffic for a typical day as historical data is usually available for typical days, whereas each disaster and evacuation are typically one-off or rare events. Damage due to a disaster, combined with a sudden increase of demand due to the evacuation of people will likely result in increased pressure on the remaining, potentially fragmented, infrastructure. The lessons learnt from evacuation modelling can assist traffic operation teams and local authorities to provide safer and more efficient planning. The development of pervasive personal digital devices such as phones, watches, and headphones which can be interconnected with technologies such as Bluetooth, has led to a disruptive change in the ways in which local governments can monitor traffic flows within their cities. Moreover, modern vehicles and navigation systems can interconnect to the personal devices of drivers and passengers primarily via Bluetooth technologies. By continuously monitoring such devices when they are discoverable and in range, traffic patterns can be estimated based on, not only the volume of detection, but also other characteristics of the devices that can be used to give more refined estimates of the real underlying traffic flows. This thesis examines Bluetooth traffic data collected from Bluetooth Traffic Monitoring Systems (BTMS) for modelling and monitoring the urban traffic. BTMS can monitor and track individual detected vehicles through a city. Installation, processing, data transmission, and maintenance of BTMS are easier, quicker and cheaper than existing standard monitoring systems such as CCTV cameras and inductive loops. Inductive loops are typically point-wise traffic monitoring systems that are installed in the roads and can measure the traffic flow. However, the use of BTMS devices presents several challenges: not every vehicle has a detectable device, some have many, and there are devices carried by pedestrians and non-motor vehicles as well as stationary devices. This thesis enumerates and investigates these challenges through statistical modelling, various protocols for cleaning and data preparation, dynamic estimation of the detection rate, and simulation through the case study of the city of Wellington, New Zealand. The city of Wellington experienced damage from the 2016 Kaikoura earthquake (a magnitude 7.8 earthquake), which led to road closures and other infrastructure damage. As part of modelling, performance evaluation, and identifying impacted routes by the 2016 Kaikoura earthquake, this thesis analyses three weeks of BTMS data from the periods before and after the earthquake. Furthermore, this thesis proposes a multi-disciplinary dynamic traffic modelling (TFDA2M) framework and evaluates the performance of TFDA2M on various large-scale evacuation scenarios. These scenarios cover a wide range of real-world use cases which may occur during a disaster such as power failure, an abrupt increase in demand, and damage to the main transportation infrastructure. The findings of this thesis highlight an immediate need for preparations of a large-scale evacuation planning for Wellington to mitigate the consequences of a large-scale evacuation due to a future disaster.  Moreover, TFDA2M can assist traffic operation managers and authorities in making smarter decisions (both quantitative and spatially) through the simulation process. Since TFDA2M has a flexible schema, it can be set to monitor, assess, and manage the traffic flow on a daily basis and disaster occasions.</p>

scholarly journals Using Bluetooth Detectors to Monitor Urban Traffic Flow with Applications to Traffic Management

2021 ◽  
Author(s):  
◽  
Mohsen Hajsalehi Sichani
Keyword(s):  
Traffic Flow ◽  
Large Scale ◽  
Traffic Monitoring ◽  
Urban Traffic ◽  
Monitoring Systems ◽  
Traffic Modelling ◽  
Typical Day ◽  
Kaikoura Earthquake ◽  
Traffic Operation ◽  
The City

<p>A comprehensive traffic monitoring system can assist authorities in identifying parts of a road transportation network that exhibit poor performance. In addition to monitoring, it is essential to develop a localized and efficient analytical transportation model that reflects various network scenarios and conditions. A comprehensive transportation model must consider various components such as vehicles and their different mechanical characteristics, human and their diverse behaviours, urban layouts and structures, and communication and transportation infrastructure and their limitations. Development of such a system requires a bringing together of ideas, tools, and techniques from multiple overlapping disciplines such as traffic and computer engineers, statistics, urban planning, and behavioural modelling. In addition to modelling of the urban traffic for a typical day, development of a large-scale emergency evacuation modelling is a critical task for an urban area as this assists traffic operation teams and local authorities to identify the limitations of traffic infrastructure during an evacuation process through examining various parameters such as evacuation time. In an evacuation, there may be severe and unpredictable damage to the infrastructure of a city such as the loss of power, telecommunications and transportation links. Traffic modelling of a large-scale evacuation is more challenging than modelling the traffic for a typical day as historical data is usually available for typical days, whereas each disaster and evacuation are typically one-off or rare events. Damage due to a disaster, combined with a sudden increase of demand due to the evacuation of people will likely result in increased pressure on the remaining, potentially fragmented, infrastructure. The lessons learnt from evacuation modelling can assist traffic operation teams and local authorities to provide safer and more efficient planning. The development of pervasive personal digital devices such as phones, watches, and headphones which can be interconnected with technologies such as Bluetooth, has led to a disruptive change in the ways in which local governments can monitor traffic flows within their cities. Moreover, modern vehicles and navigation systems can interconnect to the personal devices of drivers and passengers primarily via Bluetooth technologies. By continuously monitoring such devices when they are discoverable and in range, traffic patterns can be estimated based on, not only the volume of detection, but also other characteristics of the devices that can be used to give more refined estimates of the real underlying traffic flows. This thesis examines Bluetooth traffic data collected from Bluetooth Traffic Monitoring Systems (BTMS) for modelling and monitoring the urban traffic. BTMS can monitor and track individual detected vehicles through a city. Installation, processing, data transmission, and maintenance of BTMS are easier, quicker and cheaper than existing standard monitoring systems such as CCTV cameras and inductive loops. Inductive loops are typically point-wise traffic monitoring systems that are installed in the roads and can measure the traffic flow. However, the use of BTMS devices presents several challenges: not every vehicle has a detectable device, some have many, and there are devices carried by pedestrians and non-motor vehicles as well as stationary devices. This thesis enumerates and investigates these challenges through statistical modelling, various protocols for cleaning and data preparation, dynamic estimation of the detection rate, and simulation through the case study of the city of Wellington, New Zealand. The city of Wellington experienced damage from the 2016 Kaikoura earthquake (a magnitude 7.8 earthquake), which led to road closures and other infrastructure damage. As part of modelling, performance evaluation, and identifying impacted routes by the 2016 Kaikoura earthquake, this thesis analyses three weeks of BTMS data from the periods before and after the earthquake. Furthermore, this thesis proposes a multi-disciplinary dynamic traffic modelling (TFDA2M) framework and evaluates the performance of TFDA2M on various large-scale evacuation scenarios. These scenarios cover a wide range of real-world use cases which may occur during a disaster such as power failure, an abrupt increase in demand, and damage to the main transportation infrastructure. The findings of this thesis highlight an immediate need for preparations of a large-scale evacuation planning for Wellington to mitigate the consequences of a large-scale evacuation due to a future disaster.  Moreover, TFDA2M can assist traffic operation managers and authorities in making smarter decisions (both quantitative and spatially) through the simulation process. Since TFDA2M has a flexible schema, it can be set to monitor, assess, and manage the traffic flow on a daily basis and disaster occasions.</p>

TripRes

2021 ◽  
Vol 17 (2) ◽  
pp. 1-21
Author(s):  
Xiaolong Xu ◽  
Zijie Fang ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Qiang He ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Discrete Distribution ◽  
Resource Provisioning ◽  
Path Selection ◽  
Urban Traffic ◽  
Traffic Flows ◽  
Multimedia Services ◽  
Traffic Flow Prediction ◽  
The City ◽  
Multimedia Surveillance

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

Oosterweel – from an underground and even underwater road infrastructure plan to an urban transformation project

2021 ◽  
Author(s):  
Luc Hellemans
Keyword(s):  
Traffic Flow ◽  
Large Scale ◽  
Good Reason ◽  
Urban Transformation ◽  
Construction Site ◽  
Successful Completion ◽  
Road Infrastructure ◽  
Living Space ◽  
The City ◽  
Made In

<p>For the coming ten years, the heart of Europe will turn into a gigantic construction site for works on one of the largest hubs of the continent: Antwerp. The Oosterweel Link is the project whereby the motorway ring around Antwerp is undergoing a metamorphosis to reinvigorate traffic flow and add living space to the City. The project had come to a standstill for several years as a result of protests by assertive citizens, but was given a second lease of life following a large-scale participation project.</p><p>To ensure its successful completion, unparalleled efforts are being made in the field and in the area of digitization. It is therefore with good reason that in Belgium the project is referred to as “the construction site of the century”.</p>

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals Spectral Analysis of Traffic Functions in Urban Areas

2015 ◽  
Vol 27 (6) ◽  
pp. 477-484 ◽  
Author(s):  
Florin Nemtanu ◽  
Ilona Madalina Costea ◽  
Catalin Dumitrescu
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Fourier Analysis ◽  
Traffic Flow ◽  
Traffic Control ◽  
Urban Areas ◽  
Urban Traffic ◽  
The Fourier Transform ◽  
The City ◽  
Different Time Scales

The paper is focused on the Fourier transform application in urban traffic analysis and the use of said transform in traffic decomposition. The traffic function is defined as traffic flow generated by different categories of traffic participants. A Fourier analysis was elaborated in terms of identifying the main traffic function components, called traffic sub-functions. This paper presents the results of the method being applied in a real case situation, that is, an intersection in the city of Bucharest where the effect of a bus line was analysed. The analysis was done using different time scales, while three different traffic functions were defined to demonstrate the theoretical effect of the proposed method of analysis. An extension of the method is proposed to be applied in urban areas, especially in the areas covered by predictive traffic control.

scholarly journals Traffic Flow Analysis and Management

2021 ◽  
Author(s):  
Tsutomu Tsuboi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Flow Analysis ◽  
Traffic Monitoring ◽  
Diving Behavior ◽  
Low Carbon ◽  
Gujarat State ◽  
Traffic Condition ◽  
Transportation Demand ◽  
The City

This research is about joint government founded program between Japan and India or Science and Technology Research Partnership for Sustainable development (SATREPS). The purpose of this research is to establish Low Carbon Transportation in developing countries and we choose one of major city in India, where it is Ahmedabad city of Gujarat state—west cost of India. In order to approach the target, we need to understand the current situation of traffic condition in the city. The current traffic condition in India is some chaotic because of their different driving behavior compared with the advanced countries. It is becoming the chaotic traffic condition in India by not only diving behavior during investigation of this research. The main reason of the traffic congestion comes from the unbalance between growing transportation demand and its insufficient infrastructure preparation. In this chapter, it introduces the current traffic condition based on four years monitoring of the traffic by the traffic monitoring cameras and comparison by the traffic flow theory at first. Then it introduces the new traffic analysis method especially for its traffic congestion analysis and its parameters. After the traffic congestion analysis, it summarizes conclusion and our next step from the experience.

Application research of urban subway traffic mode based on behavior entropy in the background of big data

2021 ◽  
pp. 1-14
Author(s):  
Wanxin Hu ◽  
Fen Cheng
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Rapid Development ◽  
Urban Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Time Optimization ◽  
The City

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

Analysis of Dynamic Impedance Functions in Urban Traffic Flow Guidance System

2012 ◽  
Vol 238 ◽  
pp. 503-506 ◽  
Author(s):  
Zhi Cheng Li
Keyword(s):  
Traffic Flow ◽  
Network Model ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Guidance System ◽  
Traffic Network ◽  
Dynamic Traffic ◽  
Run Time

The successful application of Intelligent Transportation Systems (ITS) depends on the traffic flow at any time with high-precision and large-scale assessments, it is necessary to create a dynamic traffic network model to evaluate and forecast traffic. Dynamic route choice model sections of the run-time function are very important to the dynamic traffic network model. To simplify the dynamic traffic modeling, improve the calculation accuracy and save computation time, the flow on the section of the interrelationship between the exit flow and number of vehicles are analyzed, a run-time functions into the flow using only sections of the said sections are established.

scholarly journals Long-term urban traffic monitoring based on wireless multi-sensor network

2020 ◽  
Vol 10 (1) ◽  
pp. 197-208
Author(s):  
Wiktoria Loga-Księska ◽  
Justyna Sordyl ◽  
Artur Ryguła
Keyword(s):  
Sensor Network ◽  
Urban Areas ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Transport Systems ◽  
City Centre ◽  
The City

AbstractIncreasing the number of vehicles on the road network and the growing popularity of sustainable development of urban areas have resulted in the need for implementing efficient and cost-effective traffic measurement methods. From the perspective of traffic management, up-to-date information about vehicle density and access to historical data are the key components of traffic variability analyses. Rapid technological development based on Intelligent Transport Systems (ITS) has popularised the wireless sensor networks (WSN) application. The solution enables continuous monitoring of selected area using multiple wireless and low-cost sensors connected within a network. Those systems are dynamically evolving tools for solving an effective traffic management issues in city centres and urban environments. In the study, authors have performed a traffic variability and its dynamics analysis in a selected area using a multi-sensor network for traffic volume monitoring. The article presents the results of research conducted between years 2015 - 2018 throughout the city of Bielsko-Biala with the support of OnDynamic multimodal system. Within the context of the analyses, basic traffic parameters have been determined and variability trends have been identified on selected road sections. Long-term research indicated the minor variation in a number of vehicle detections and relatively stable traffic volume in the city centre during the analysis period.

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