On the Feasibility of Social Network-based Pollution Sensing in ITSs

Intense vehicular traffic is recognized as a global societal problem, with a multifaceted influence on the quality of life of a person. Intelligent Transportation Systems (ITS) can play an important role in combating such problem, decreasing pollution levels and, consequently, their negative effects. One of the goals of ITSs, in fact, is that of controlling traffic flows, measuring traffic states, providing vehicles with routes that globally pursue low pollution conditions. How such systems measure and enforce given traffic states has been at the center of multiple research efforts in the past few years. Although many different solutions have been proposed, very limited effort has been devoted to exploring the potential of social network analysis in such context. Social networks, in general, provide direct feedback from people and, as such, potentially very valuable information. A post that tells, for example, how a person feels about pollution at a given time in a given location, could be put to good use by an environment aware ITS aiming at minimizing contaminant emissions in residential areas. This work verifies the feasibility of using pollution related social network feeds into ITS operations. In particular, it concentrates on understanding how reliable such information is, producing an analysis that confronts over 1,500,000 posts and pollution data obtained from on-the- field sensors over a one-year span.Comment: 10 pages, 15 figures, Transaction Forma

ABEONA monitored traffic: VANET-assisted cooperative traffic congestion forecasting

The existing mechanisms to monitor vehicular traffic, such as the use of induction loops and cameras, are expensive to deploy and maintain. Vehicular communications opens up a new world of optimization opportunities as each vehicle can be used as a sensor to measure the fundamental variables defining the traffic state (flow, density, and speed). In this article, we propose ABEONA, a beacon-based traffic congestion algorithm and also the name of the Roman goddess of journey, which captures the current and recent-past traffic trends to forecast the near-future road conditions. Compared to the existing monitoring approaches, ABEONA allows for the estimation of the vehicular density and reduces installation and maintenance costs. ABEONA's algorithm incurs low overhead and enables drivers to use forecast traffic congestion events to replan their route accordingly.Publicad

STUDI TENTANG PEMODELAN ARUS LALU LINTAS

Permasalahan transportasi saat ini masih menjadi pemasalahan utama pada setiap negara, khususnya negara berkembang. Masalah transportasi dihadapkan pada fenomena kemacetan, banyaknya polusi yang dihasilkan oleh kendaraan, sampai kepada masih tingginya tingkat kecelakaan lalu lintas tiap tahunnya. Hal ini, bukan saja disebabkan oleh perilaku pengemudi jalan raya saja, akan tetapi perencanaan arus lalu lintas pun menjadi salah satu faktor yang mempengaruhinya. Salah satu alternatif penyelesaian untuk dapat mengatur dan memanajemen arus lalu lintas adalah dengan memodelkan arus lalu lintas serta mensimulasikannya dalam komputer sehingga dapat diperoleh prediksi-prediksi yang akan terjadi pada simulasi tersebut. Studi literatur mengenai pemodelan dan simulasi arus lalu lintas terus berkembang sejak setengah abad yang lalu dalam upaya memperoleh sebuah pemodelan yang akurat dan mewakili fenomena yang terjadi sebenarnya. Pemodelan arus lalu lintas berbasis komputer dapat dibagi menjadi tiga skala utama, yaitu: mikroskopik, mesoskopik dan makroskopik. Pada skala mikroskopik, pemodelan arus lalu lintas digambarkan sedetail mungkin yang mencakup perilaku setiap kendaraan dan interaksinya. Pada paper ini dilakukan survey terhadap penelitian terdahulu yang membahas mengenai pemodelan arus lalu lintas pada skala mikroskopik. Pada bagian pertama akan dijelaskan gambaran dan pemahaman mengenai pemodelan arus lalu lintas, pemahaman mengenai model mikroskopik arus dan beberapa penelitian mengenai model yang sudah dikembangkan untuk simulasi mikroskopik beberapa tahun terakhir. Selanjutnya, dilakukan pembahasan mengenai pemodelan arus mikroskopik dihubungkan dengan permasalahan transportasi yang ada di Indonesia. Pada paper ini juga memberikan kemungkinan pengembangan penelitian lebih lanjut untuk model mikroskopik lalu lintas

Are You Responsible for Traffic Congestion? A Systematic Review of the Socio-technical Perspective of Smart Mobility Services

A large amount of the pollution of modern cities is caused by individual transportation. Hence, many road users suffer from stress, emissions and noise. Smart mobility services can help improving the situa-tion by distributing traffic more consistently across different routes, times, and transportation modes. These services comprise two dimensions, a technical and a socio-technical. The latter addresses the road user’s role as data and knowledge provider and stresses the road user’s role in actively contributing to relieved traffic. As such, road users display one of the strongest levers to sustainably relieve traffic both in terms of knowledge providers and traffic actors. Using a systematic analysis of 28 publications, we show that existing SMob services show several chal-lenges related to the involvement of road users. We call for more research on SMob services that account for long-term user involvement e.g. by positively in-fluences road users’ practices and routines

Analysis of Causes on Suburban Traffic Congestion Reference to Ragama town, Sri Lanka

A developed transportation system, connecting rail and road is an outcome of the substantial concentration of inter-related activities in an urban area. Thus, traffic congestion is inevitable. Congestion increases the travel time and decreases the efficiency of activities, consequently, congestion increases the economic cost of activities. Hence, traffic congestion is a problem for society and to the economy. A huge volume of literature is available on traffic and related research along with few concentrating on causes of congestion. Yet no studies focused on the sub-urban centers. Therefore, this research aims to analyse the causes on traffic congestion in one of the sub-urban areas of Sri Lanka. The study focused on four major issues, such as physical, technical, social, and legal. Data were collected using a five-point Likert scale questionnaire from conveniently selected 70 commuters, and in-depth interviews were conducted with five professional experts on the same factors. Analysis completed in both quantitative and qualitative approaches and the findings indicate physical and technical issues are significant in both analysis. Hence, the policy makers advise to consider the remedies more on physical and technical factors to improve the smooth flow of traffic

Social Cost Guarantees in Smart Route Guidance

We model and study the problem of assigning traffic in an urban road network infrastructure. In our model, each driver submits their intended destination and is assigned a route to follow that minimizes the social cost (i.e., travel distance of all the drivers). We assume drivers are strategic and try to manipulate the system (i.e., misreport their intended destination and/or deviate from the assigned route) if they can reduce their travel distance by doing so. Such strategic behavior is highly undesirable as it can lead to an overall suboptimal traffic assignment and cause congestion. To alleviate this problem, we develop moneyless mechanisms that are resilient to manipulation by the agents and offer provable approximation guarantees on the social cost obtained by the solution. We then empirically test the mechanisms studied in the paper, showing that they can be effectively used in practice in order to compute manipulation resistant traffic allocations

REVIEW OF MICROSCOPIC MODEL FOR TRAFFIC FLOW

ABSTRACT Today, the problem of cities urban transportation is becoming something we have to face in our daily life. In Indonesia, traffic congestion is increasingly serious. Several economic and social motivations can be related to the need to minimize the time spent in vehicles for transportation and consequently their related pollution problems. Due to these motivations, the literature on traffic phenomena is already vast and characterized by contributions covering modeling aspects, statement of problems, qualitative analysis, and particularly developed simulation generated by applications. This paper will provides a several literature review of microscopic model based on their utilities, including the critical review about the modeling approaches. Furthermore, some practical issues such as potential for future model improvement using existing and emerging data collection technologies is identified based on Indonesian traffic characteristics and will be presented as a contribution from this paper

Internet of Vehicles and Autonomous Connected Car - Privacy and Security Issues

In the Intelligent Vehicle Grid, the car is becoming a formidable sensor platform, absorbing information from the environment, from other cars (and from the driver) and feeding it to other cars and infrastructure to assist in safe navigation, pollution control and traffic management. The Vehicle Grid essentially becomes an Internet of Things (IOT), which we call Internet of Vehicles (IOV), capable to make its own decisions about driving customers to their destinations. Like other important IOT examples (e.g., smart buildings), the Internet of Vehicles will not merely upload data to the Internet using V2I. It will also use V2V communications between peers to complement on board sensor inputs and provide safe and efficient navigation. In this paper, we first describe several vehicular applications that leverage V2V and V2I. Communications with infrastructure and with other vehicles, however, can create privacy and security violations. In the second part of the paper we address these issues and more specifically focus on the need to guarantee location privacy to mobile users. We argue on the importance of creating public, open ``smart city" data repositories for the research community and propose privacy preserving techniques for the anonymous uploading of urban sensor data from vehicles

Dynamic Vehicular Route Guidance Using Traffic Prediction Information

We propose a dynamic vehicular routing algorithm with traffic prediction for improved routing performance. The primary idea of our algorithm is to use real-time as well as predictive traffic information provided by a central routing controller. In order to evaluate the performance, we develop a microtraffic simulator that provides road networks created from real maps, routing algorithms, and vehicles that travel from origins to destinations depending on traffic conditions. The performance is evaluated by newly defined metric that reveals travel time distributions more accurately than a commonly used metric of mean travel time. Our simulation results show that our dynamic routing algorithm with prediction outperforms both Static and Dynamic without prediction routing algorithms under various traffic conditions and road configurations. We also include traffic scenarios where not all vehicles comply with our dynamic routing with prediction strategy, and the results suggest that more than half the benefit of the new routing algorithm is realized even when only 30% of the vehicles comply