Evaluating Origin–Destination Matrices Obtained from CDR Data

Understanding and correctly modeling urban mobility is a crucial issue for the development of smart cities. The estimation of individual trips from mobile phone positioning data (i.e., call detail records (CDR)) can naturally support urban and transport studies as well as marketing applications. Individual trips are often aggregated in an origin–destination (OD) matrix counting the number of trips from a given origin to a given destination. In the literature dealing with CDR data there are two main approaches to extract OD matrices from such data: (a) in time-based matrices, the analysis focuses on estimating mobility directly from a sequence of CDRs; (b) in routine-based matrices (OD by purpose) the analysis focuses on routine kind of movements, like home-work commute, derived from a trip generation model. In both cases, the OD matrix measured by CDR counts is scaled to match the actual number of people moving in the area, and projected to the road network to estimate actual flows on the streets. In this paper, we describe prototypical approaches to estimate OD matrices, describe an actual implementation, and present a number of experiments to evaluate the results from multiple perspectives.

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Broadening Understanding on Managing the Communication Infrastructure in Vehicular Networks: Customizing the Coverage Using the Delta Network

Future Internet ◽

10.3390/fi11010001 ◽

2018 ◽

Vol 11 (1) ◽

pp. 1 ◽

Cited By ~ 9

Author(s):

Cristiano Silva ◽

Lucas Silva ◽

Leonardo Santos ◽

João Sarubbi ◽

Andreas Pitsillides

Keyword(s):

Urban Areas ◽

Road Network ◽

Vehicular Networks ◽

Urban Environments ◽

Urban Mobility ◽

Circle Pattern ◽

The Road ◽

The Past ◽

Communication Devices ◽

Definition Of

Over the past few decades, the growth of the urban population has been remarkable. Nowadays, 50% of the population lives in urban areas, and forecasts point that by 2050 this number will reach 70%. Today, 64% of all travel made is within urban environments and the total amount of urban kilometers traveled is expected to triple by 2050. Thus, seeking novel solutions for urban mobility becomes paramount for 21st century society. In this work, we discuss the performance of vehicular networks. We consider the metric Delta Network. The Delta Network characterizes the connectivity of the vehicular network through the percentage of travel time in which vehicles are connected to roadside units. This article reviews the concept of the Delta Network and extends its study through the presentation of a general heuristic based on the definition of scores to identify the areas of the road network that should receive coverage. After defining the general heuristic, we show how small changes in the score computation can generate very distinct (and interesting) patterns of coverage, each one suited to a given scenario. In order to exemplify such behavior, we propose three deployment strategies based on simply changing the computation of scores. We compare the proposed strategies to the intuitive strategy of allocating communication units at the most popular zones of the road network. Experiments show that the strategies derived from the general heuristic provide higher coverage than the intuitive strategy when using the same number of communication devices. Moreover, the resulting pattern of coverage is very interesting, with roadside units deployed a circle pattern around the traffic epicenter.

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Visualization of Urban Mobility Data from Intelligent Transportation Systems

Sensors ◽

10.3390/s19020332 ◽

2019 ◽

Vol 19 (2) ◽

pp. 332 ◽

Cited By ~ 15

Author(s):

Thiago Sobral ◽

Teresa Galvão ◽

José Borges

Keyword(s):

Intelligent Transportation Systems ◽

Smart Cities ◽

Intelligent Transportation ◽

Transportation Systems ◽

Urban Mobility ◽

Multiple Perspectives ◽

Data Types ◽

Mobility Data ◽

Extensive Body ◽

Visualization Techniques

Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings.

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Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i2.570 ◽

2018 ◽

Vol 8 (2) ◽

pp. 10

Author(s):

Mustapha Kabrane ◽

Salah-ddine Krit ◽

Lahoucine El Maimouni

Keyword(s):

Traffic Congestion ◽

Urban Areas ◽

Road Network ◽

Smart Cities ◽

Optimization Techniques ◽

Traffic Light ◽

Large Cities ◽

Traffic Lights ◽

The Road ◽

Road Signs

In large cities, the increasing number of vehicles private, society, merchandise, and public transport, has led to traffic congestion. Users spend much of their time in endless traffic congestion. To solve this problem, several solutions can be envisaged. The interest is focused on the system of road signs: The use of a road infrastructure is controlled by a traffic light controller, so it is a matter of knowing how to make the best use of the controls of this system (traffic lights) so as to make traffic more fluid. The values of the commands computed by the controller are determined by an algorithm which is ultimately, only solves a mathematical model representing the problem to be solved. The objective is to make a study and then the comparison on the optimization techniques based on artificial intelligence1 to intelligently route vehicle traffic. These techniques make it possible to minimize a certain function expressing the congestion of the road network. It can be a function, the length of the queue at intersections, the average waiting time, also the total number of vehicles waiting at the intersection

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An Incentive Based Dynamic Ride-Sharing System for Smart Cities

Smart Cities ◽

10.3390/smartcities4020028 ◽

2021 ◽

Vol 4 (2) ◽

pp. 532-547

Author(s):

Abu Saleh Md Bakibillah ◽

Yi Feng Paw ◽

Md Abdus Samad Kamal ◽

Susilawati Susilawati ◽

Chee Pin Tan

Keyword(s):

Control Systems ◽

Traffic Control ◽

Smart Cities ◽

Flow Behavior ◽

Urban Mobility ◽

Microscopic Traffic Simulation ◽

The Road ◽

Road Capacity ◽

Ride Sharing ◽

Traffic Performance

Connected and automated vehicle (CAV) technology, along with advanced traffic control systems, cannot ensure congestion-free traffic when the number of vehicles exceeds the road capacity. To address this problem, in this paper, we propose a dynamic ride-sharing system based on incentives (for both passengers and drivers) that incorporates travelers of similar routes and time schedules on short notice. The objective is to reduce the number of private vehicles on urban roads by utilizing the available seats properly. We develop a mobile-cloud architecture-based system that enables real-time ride-sharing. The effectiveness of the proposed system is evaluated through microscopic traffic simulation using Simulation of Urban Mobility (SUMO) considering the traffic flow behavior of a real smart city. Moreover, we develop a lab-scale experimental prototype in the form of Internet of Things (IoT) network. The simulation results show that the proposed system reduces fuel consumption, CO2 and CO emissions, and average waiting time of vehicles significantly, while increasing the vehicle’s average speed. Remarkably, it is found that only 2–10% ride-sharing can improve the overall traffic performance.

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ANALYSIS OF THE ROAD NETWORK STRUCTURES BASED ON STREET CONNECTIVITY

Contexto ◽

10.29105/contexto14.20-6 ◽

2020 ◽

Vol 14 (20) ◽

Author(s):

María Erándi Flores Romero ◽

Irving Omar Morales Agiss ◽

Liliana Beatriz Sosa Compean

Keyword(s):

Community Detection ◽

Road Network ◽

Public Policies ◽

Detection Algorithm ◽

Street Connectivity ◽

Urban Mobility ◽

Road Infrastructure ◽

The Road ◽

Community Detection Algorithm ◽

The City

The following article proposes a method to identify structures inside a road network with a flow-base community detection algorithm implemented on a graph representing the city road network. According to the results obtained in the cities of Mexico and Monterrey, the method effectively divides road infrastructure into several communities and preserves geographical neighboring. The frontiers of communities match administrative divisions along with other frontiers inside the city. The identification of communities could be useful to study the heterogeneity of street connectivity inside the city which could lead to improvements in urban mobility or even the application of public policies.

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Geofencing to Enable Differentiated Road User Charging

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121995510 ◽

2021 ◽

pp. 036119812199551

Author(s):

Petter Arnesen ◽

Hanne Seter ◽

Ørjan Tveit ◽

Mats Myhrvold Bjerke

Keyword(s):

Travel Behavior ◽

Road Network ◽

Road User ◽

Urban Mobility ◽

Dual Purpose ◽

The Road ◽

Transport Services ◽

On The Road ◽

The Cost ◽

Road User Charging

Tolling normally has a dual purpose in Norway. Its first goal is to finance a project or an improvment in the transport services offered in an area, for instance extend public transport services. The second goal is to change travel behavior, encouraging drivers of private cars to use other more environmentally friendly modes. Today, this tolling system is based on fixed points on the road network which are not necessarily able to record all road usage evenly. Within the GeoSUM (Geofencing for Smart Urban Mobility) research project, a distance and fuel differentiated road user charging scheme has been piloted. Instead of fixed point tolling, this system enables the driver to perceive that the cost is directly related to how much gasoline or electricity is used on the road network. The key technology for this system is geofencing, and the pilot results show that the proposed system did indeed increase the amount electricity used for driving inside the geofence zones, reducing in turn the amount of fossil-based fuel used.

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Broadening Understanding on Managing the Communication Infrastructure in Vehicular Networks: Customizing the Coverage Using the Delta Network

10.20944/preprints201811.0294.v1 ◽

2018 ◽

Author(s):

Cristiano M. Silva ◽

Lucas D. Silva ◽

Leonardo A. L. Santos ◽

João F. M. Sarubbi ◽

Andreas Pitsillides

Keyword(s):

Urban Areas ◽

Road Network ◽

Vehicular Networks ◽

Vehicular Network ◽

Urban Environments ◽

Urban Mobility ◽

Circle Pattern ◽

The Road ◽

The Past ◽

Definition Of

Over the past few decades, the growth of the urban population has been remarkable. Nowadays, 50% of the population lives in urban areas, and forecasts point that by 2050 this number will reach 70%. Today, 64% of all travel made is within urban environments and the total amount of urban kilometers traveled is expected to triple by 2050. Thus, seeking novel solutions for urban mobility becomes paramount for 21st century society. In this work, we discuss the performance of vehicular networks. We consider the metric Delta Network. The Delta Network characterizes the connectivity of the vehicular network through the percentage of travel time in which vehicles are connected to roadside units. This article reviews the concept of Delta Network and extends its study through the presentation of a general heuristic based on the definition of scores to identify the areas of the road network that should receive coverage. After defining the general heuristic, we show how small changes in the score computation can generate very distinct (and interesting) patterns of coverage, each one suited to a given scenario. In order to exemplify such behavior, we propose three deployment strategies based on simply changing the computation of scores. The results show that the strategies derived from the general heuristic are very interesting, all of them deploying roadside units in a circle pattern around the traffic epicenter.

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Traffic congestion propagation identification method in smart cities

Infocommunications journal ◽

10.36244/icj.2021.1.6 ◽

2021 ◽

Vol 13 (1) ◽

pp. 45-57

Author(s):

Attila M. Nagy ◽

Vilmos Simon

Keyword(s):

Traffic Congestion ◽

Traffic Management ◽

Road Network ◽

Smart Cities ◽

Network Data ◽

Professional Literature ◽

Large Cities ◽

Traffic Jam ◽

The Road ◽

Traffic Jams

Managing the frequent traffic congestion (traffic jams) of the road networks of large cities is a major challenge for municipal traffic management organizations. In order to manage these situations, it is crucial to understand the processes that lead to congestion and propagation, because the occurrence of a traffic jam does not merely paralyze one street or road, but could spill over onto the whole vicinity (even an entire neighborhood). Solutions can be found in professional literature, but they either oversimplify the problem, or fail to provide a scalable solution. In this article, we describe a new method that not only provides an accurate road network model, but is also a scalable solution for identifying the direction of traffic congestion propagation. Our method was subjected to a detailed performance analysis, which was based on real road network data. According to testing, our method outperforms the ones that have been used to date.

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Efficient Occluded Road Extraction from High-Resolution Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs13244974 ◽

2021 ◽

Vol 13 (24) ◽

pp. 4974

Author(s):

Dejun Feng ◽

Xingyu Shen ◽

Yakun Xie ◽

Yangge Liu ◽

Jian Wang

Keyword(s):

Intelligent Transportation Systems ◽

Road Network ◽

Smart Cities ◽

Heuristic Method ◽

Intelligent Transportation ◽

Transportation Systems ◽

Road Extraction ◽

Learning Models ◽

The Road ◽

The Common

Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35–12.8% and 2.41–9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas.

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Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities

Energies ◽

10.3390/en14164933 ◽

2021 ◽

Vol 14 (16) ◽

pp. 4933

Author(s):

Miguel Campaña ◽

Esteban Inga ◽

Jorge Cárdenas

Keyword(s):

Traffic Engineering ◽

Road Network ◽

Smart Cities ◽

Minimum Cost ◽

High Energy ◽

Urban Traffic ◽

Vehicular Traffic ◽

Commodity Flow ◽

The Road ◽

Charging Stations

Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research.

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