scholarly journals GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

2021 ◽  
Vol 13 (4) ◽  
pp. 544
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Gnss Receivers ◽  
Multipath Signal ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

scholarly journals GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

2020 ◽  
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Multipath Signal ◽  
Multi Agent ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques are recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiment requiring numbers of devices is hard to be conducted, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

Quality indicator of the smart city transportation and logistics system

2020 ◽  
Vol 19 (11) ◽  
pp. 2116-2135
Author(s):  
G.V. Savin
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Urban Areas ◽  
Human Life ◽  
Life Quality ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Logistics System ◽  
Organization And Management

Subject. The article considers functioning and development of process flows of transportation and logistics system of a smart city. Objectives. The study identifies factors and dependencies of the quality of human life on the organization and management of stream processes. Methods. I perform a comparative analysis of previous studies, taking into account the uniquely designed results, and the econometric analysis. Results. The study builds multiple regression models that are associated with stream processes, highlights interdependent indicators of temporary traffic and pollution that affect the indicator of life quality. However, the identified congestion indicator enables to predict the time spent in traffic jams per year for all participants of stream processes. Conclusions. The introduction of modern intelligent transportation systems as a component of the transportation and logistics system of a smart city does not fully solve the problems of congestion in cities at the current rate of urbanization and motorization. A viable solution is to develop cooperative and autonomous intelligent transportation systems based on the logistics approach. This will ensure control over congestion, the reduction of which will contribute to improving the life quality of people in urban areas.

Simplex-Based Calibration of Traffic Microsimulation Models with Intelligent Transportation Systems Data

2003 ◽  
Vol 1855 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Kyu-Ok Kim ◽  
L. R. Rilett
Keyword(s):  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
System Modeling ◽  
Intelligent Transportation ◽  
Simplex Algorithm ◽  
Transportation System ◽  
Transportation Systems ◽  
Test Bed ◽  
Morning Peak ◽  
Microsimulation Models

In recent years, microsimulation has become increasingly important in transportation system modeling. A potential issue is whether these models adequately represent reality and whether enough data exist with which to calibrate these models. There has been rapid deployment of intelligent transportation system (ITS) technologies in most urban areas of North America in the last 10 years. While ITSs are developed primarily for real-time traffic operations, the data are typically archived and available for traffic microsimulation calibration. A methodology, based on the sequential simplex algorithm, that uses ITS data to calibrate microsimulation models is presented. The test bed is a 23-km section of Interstate 10 in Houston, Texas. Two microsimulation models, CORSIM and TRANSIMS, were calibrated for two different demand matrices and three periods (morning peak, evening peak, and off-peak). It was found for the morning peak that the simplex algorithm had better results then either the default values or a simple, manual calibration. As the level of congestion decreased, the effectiveness of the simplex approach also decreased, as compared with standard techniques.

scholarly journals Deep Learning System for Vehicular Re-Routing and Congestion Avoidance

2019 ◽  
Vol 9 (13) ◽  
pp. 2717 ◽  
Author(s):  
Pedro Perez-Murueta ◽  
Alfonso Gómez-Espinosa ◽  
Cesar Cardenas ◽  
Miguel Gonzalez-Mendoza
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Transportation Systems ◽  
Learning System ◽  
Traffic Network ◽  
Public And Private ◽  
Traffic Jam ◽  
Private Transportation ◽  
Alternative Routes

Delays in transportation due to congestion generated by public and private transportation are common in many urban areas of the world. To make transportation systems more efficient, intelligent transportation systems (ITS) are currently being developed. One of the objectives of ITS is to detect congested areas and redirect vehicles away from them. However, most existing approaches only react once the traffic jam has occurred and, therefore, the delay has already spread to more areas of the traffic network. We propose a vehicle redirection system to avoid congestion that uses a model based on deep learning to predict the future state of the traffic network. The model uses the information obtained from the previous step to determine the zones with possible congestion, and redirects the vehicles that are about to cross them. Alternative routes are generated using the entropy-balanced k Shortest Path algorithm (EBkSP). The proposal uses information obtained in real time by a set of probe cars to detect non-recurrent congestion. The results obtained from simulations in various scenarios have shown that the proposal is capable of reducing the average travel time (ATT) by up to 19%, benefiting a maximum of 38% of the vehicles.

scholarly journals How Can Smart Mobility Innovations Alleviate Transportation Disadvantage? Assembling a Conceptual Framework through a Systematic Review

2020 ◽  
Vol 10 (18) ◽  
pp. 6306 ◽  
Author(s):  
Luke Butler ◽  
Tan Yigitcanlar ◽  
Alexander Paz
Keyword(s):  
Conceptual Framework ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Technological Innovations ◽  
Potential Solution ◽  
Wide Range ◽  
Smart Mobility

Transportation disadvantage is about the difficulty accessing mobility services required to complete activities associated with employment, shopping, business, essential needs, and recreation. Technological innovations in the field of smart mobility have been identified as a potential solution to help individuals overcome issues associated with transportation disadvantage. This paper aims to provide a consolidated understanding on how smart mobility innovations can contribute to alleviate transportation disadvantage. A systematic literature review is completed, and a conceptual framework is developed to provide the required information to address transportation disadvantage. The results are categorized under the physical, economic, spatial, temporal, psychological, information, and institutional dimensions of transportation disadvantage. The study findings reveal that: (a) Primary smart mobility innovations identified in the literature are demand responsive transportation, shared transportation, intelligent transportation systems, electric mobility, autonomous vehicles, and Mobility-as-a-Services. (b) Smart mobility innovations could benefit urban areas by improving accessibility, efficiency, coverage, flexibility, safety, and the overall integration of the transportation system. (c) Smart mobility innovations have the potential to contribute to the alleviation of transportation disadvantage. (d) Mobility-as-a-Service has high potential to alleviate transportation disadvantage primarily due to its ability to integrate a wide-range of services.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

Assessment of Rural Intelligent Transportation System Wireless Communications Solutions

2000 ◽  
Vol 1739 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Qingyan Yang ◽  
Virginia Sisiopiku ◽  
Jim A. Arnold ◽  
Paul Pisano ◽  
Gary G. Nelson
Keyword(s):  
Wireless Communications ◽  
Wireless Communication ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Communication Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Future Research ◽  
Wireless Communication Systems ◽  
Rural Environments

Rural transportation systems have different features and needs than their urban counterparts. To address safety and efficiency concerns in rural environments, advanced rural transportation systems (ARTS) test and deploy appropriate intelligent transportation systems (ITS) technologies, many of which require communication support. However, wireless communication systems that currently serve urban areas often are not available or suitable in rural environments. Thus, a need exists to identify communication solutions that are likely to address successfully the needs and features of ARTS applications. Current and emerging wireless communications systems and technologies have been systematically assessed with respect to rural ITS applications. Wireless communication functions associated with rural ITS functions are first identified. Then requirements for applicable communication technologies in the rural environment are defined. Existing and emerging wireless communication systems and technologies are reviewed and evaluated by a systematic process of assessing rural ITS wireless solutions. Finally, recommendations for future research and operational tests are offered. The analysis results are expected to benefit rural ITS planners by identifying suitable wireless solutions for different rural contexts.

Supervisory Decision-Making

2002 ◽  
Vol 46 (3) ◽  
pp. 318-322
Author(s):  
Dwight P. Miller ◽  
Jack Schryver ◽  
Daniel R. Tufano
Keyword(s):  
Decision Making ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Decision Aids ◽  
Organization Design ◽  
Transportation Systems ◽  
Design Guidelines ◽  
Intelligent Software ◽  
Trust In Automation ◽  
Dynamic Decisions

Supervisory Decision-Making (SDM) refers to human supervision of several semi-autonomous (nonhuman) systems in a collaborative manner to accomplish a goal. This study defined SDM and distinguished it from traditional supervisory control and decision-making. An examination of diverse literature in organization design, biology, robotics, innovation diffusion, and trust in automation, yielded no directly applicable or comprehensive models. Field observations were made of large-scale war-games, where operators interacted with semi/autonomous sensors and defense-management systems. Four cognitive models were subsequently developed describing 1) adaptive partnering with automation, 2) technology adoption, 3) trust in automation, and 4) dealing with advice from decision aids. The latter quantitatively models individual, dynamic decisions to accept or reject recommendations made by automated battlespace advisors. The anticipated benefits of this work include more effective human-robot coordination, communication, the identification of experiments, and ultimately design guidelines for robotics, intelligent software agents, intelligent transportation systems, and space exploration.

scholarly journals INVESTIGATING THE POTENTIAL OF ACTIVITY TRACKING APP DATA TO ESTIMATE CYCLE FLOWS IN URBAN AREAS

2016 ◽  
Vol XLI-B2 ◽  
pp. 515-519
Author(s):  
J. Haworth
Keyword(s):  
Temporal Variation ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Temporal Structure ◽  
Transportation Systems ◽  
Spatial And Temporal Variation ◽  
Linear Regression Models ◽  
Activity Tracking ◽  
Spatio Temporal ◽  
On The Road

Traffic congestion and its associated environmental effects pose a significant problem for large cities. Consequently, promoting and investing in green travel modes such as cycling is high on the agenda for many transport authorities. In order to target investment in cycling infrastructure and improve the experience of cyclists on the road, it is important to know where they are. Unfortunately, investment in intelligent transportation systems over the years has mainly focussed on monitoring vehicular traffic, and comparatively little is known about where cyclists are on a day to day basis. In London, for example, there are a limited number of automatic cycle counters installed on the network, which provide only part of the picture. These are supplemented by surveys that are carried out infrequently. Activity tracking apps on smart phones and GPS devices such as Strava have become very popular over recent years. Their intended use is to track physical activity and monitor training. However, many people routinely use such apps to record their daily commutes by bicycle. At the aggregate level, these data provide a potentially rich source of information about the movement and behaviour of cyclists. Before such data can be relied upon, however, it is necessary to examine their representativeness and understand their potential biases. In this study, the flows obtained from Strava Metro (SM) are compared with those obtained during the 2013 London Cycle Census (LCC). A set of linear regression models are constructed to predict LCC flows using SM flows along with a number of dummy variables including road type, hour of day, day of week and presence/absence of cycle lane. Cross-validation is used to test the fitted models on unseen LCC sites. SM flows are found to be a statistically significant (p<0.0001) predictor of total flows as measured by the LCC and the models yield R squared statistics of ~0.7 before considering spatio-temporal variation. The initial results indicate that data collected using fitness tracking apps such as Strava are a promising data source for traffic managers. Future work will incorporate the spatio-temporal structure in the data to better account for the spatial and temporal variation in the ratio of SM flows to LCC flows.

scholarly journals Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies

2020 ◽  
Author(s):  
Miguel Ribeiro ◽  
Nuno Nunes ◽  
Valentina Nisi ◽  
Johannes Schöning
Keyword(s):  
Public Transportation ◽  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Tracking System ◽  
Human Mobility ◽  
Transportation Systems ◽  
Mobility Patterns ◽  
Systematic Analysis ◽  
Long Term Study

Abstract In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.

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