Threshold queueing describes the fundamental diagram of uninterrupted traffic

Queueing due to congestion is an important aspect of road traffic. This paper provides a brief overview of queueing models for traffic and a novel threshold queue that captures the main aspects of the empirical shape of the fundamental diagram. Our numerical results characterises the sources of variation that influence the shape of the fundamental diagram

Determination of Exponential Congestion Factor of Road Traffic Flow Caused By Irregular Occurrences

The present paper deals exponential congestion model of road traffic flow caused by irregular occurrences. Congestion that is happened by unpredictable events, for example, auto collisions, handicapped vehicles, climate conditions, over burdens and unsafe materials of vehicles. On account of these sorts of sudden occasions, the travel times taken on the roadways are questionable. We established the steady state conditions based on number of vehicles on road links. The large c values of those links, M/M/1 queues model under the batch service interruptions may be used. The formulation and assumptions of the proposed models have been developed. The exponential congestion factor (ECF) models based on M/MSP/C queuing have been presented. Finally, the numerical examples have also been discussed

Behavior and Management of Stochastic Multiple-Origin-Destination Traffic Flows Sharing a Common Link

In transportation systems (e.g. highways, railways, airports), traffic flows with distinct origin-destination pairs usually share common facilities and interact extensively. Such interaction is typically stochastic due to natural fluctuations in the traffic flows. In this paper, we study the interaction between multiple traffic flows and propose intuitive but provably efficient control algorithms based on modern sensing and actuating capabilities. We decompose the problem into two sub-problems: the impact of a merging junction and the impact of a diverging junction. We use a fluid model to show that (i) appropriate choice of priority at the merging junction is decisive for stability of the upstream queues and (ii) discharging priority at the diverging junction does not affect stability. We also illustrate the insights of our analysis via an example of management of multi-class traffic flows with platooning

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

The use of real-time connected vehicles and HERE data in developing an automated freeway incident detection algorithm

Traffic incidents cause severe problems on roadways. About 6.3 million highway crashes are reported annually only in the United States, among which more than 32,000 are fatal crashes. Reducing the risk of traffic incidents is key to effective traffic incident management (TIM). Quick detection of unexpected traffic incidents on roadways contribute to quick clearance and hence improve safety. Existing techniques for the detection of freeway incidents are not reliable. This study focuses on exploring the potential of emerging connected vehicles (CV) technology in automated freeway incident detection in the mixed traffic environment. The study aims at developing an automated freeway incident detection algorithm that will take advantage of the CV technology in providing fast and reliable incident detection. Lee Roy Selmon Expressway was chosen for this study because of the THEA CV data availability. The findings of the study show that emerging CV technology generates data that are useful for automated freeway incident detection, although the market penetration rate was low (6.46%). The algorithm performance in terms of detection rate (DR) and false alarm rate (FAR) indicated that CV data resulted into 31.71% DR and zero FAR while HERE yielded a 70.95% DR and 9.02% FAR. Based on Pearson’s correlation analysis, the incidents detected by the CV data were found to be similar to the ones detected by the HERE data. The statistical comparison by ANOVA shows that there is a difference in the algorithm’s detection time when using CV data and HERE data. 17.07% of all incidents were detected quicker when using CV data compared to HERE data, while 7.32% were detected quicker when using HERE data compared to CV data

Edge-powered Assisted Driving For Connected Cars

Assisted driving for connected cars is one of the main applications that 5G-and-beyond networks shall support. In this work, we propose an assisted driving system leveraging the synergy between connected vehicles and the edge of the network infrastructure, in order to envision global traffic policies that can effectively drive local decisions. Local decisions concern individual vehicles, e.g., which vehicle should perform a lane-change manoeuvre and when; global decisions, instead, involve whole traffic flows. Such decisions are made at different time scales by different entities, which are integrated within an edge-based architecture and can share information. In particular, we leverage a queuing-based model and formulate an optimization problem to make global decisions on traffic flows. To cope with the problem complexity, we then develop an iterative, linear-time complexity algorithm called Bottleneck Hunting (BH). We show the performance of our solution using a realistic simulation framework, integrating a Python engine with ns-3 and SUMO, and considering two relevant services, namely, lane change assistance and navigation, in a real-world scenario. Results demonstrate that our solution leads to a reduction of the vehicles' travel times by 66 in the case of lane change assistance and by 20 for navigation, compared to traditional, local-coordination approaches.Comment: arXiv admin note: text overlap with arXiv:2008.0933

Modeling random traffic accidents by conservation laws

We introduce a stochastic traffic flow model to describe random traffic accidents on a singleroad. The model is a piecewise deterministic process incorporating traffic accidents and is based on ascalar conservation law with space-dependent flux function. Using a Lax-Friedrichs discretization, weshow that the total variation is bounded in finite time and provide a theoretical framework to embedthe stochastic process. Additionally, a solution algorithm is introduced to also investigate the modelnumerically

Master of Science

thesisOne of the major challenges for Traffic Operations Center (TOC) operators is to determine the nature of their response to traffic incidents. This applies to both operators' training and real traffic management. While incidents vary by location and degree of disruption, operators' responses vary by how quickly they are implemented and what degree of actions they take. Operators can react instantaneously and divert traffic from an entire highway, or simply wait and apply a mild variable message. Travelers' delay under incident conditions depends not only on incident severity, but also on the effectiveness of response to an incident. This is an analysis of a wide range of incidents and responses for the set of critical locations on a test Salt Lake Valley freeway network. It uses VISSIM microsimulation to determine optimal responses under various incident conditions. Incident severity is represented through Incident Location, Incident Duration and Lane Closure. Incident response is defined through the Response Time, and Variable Message Sign (VMS) Levels and VMS Display Time. As expected, the resulting degree of incident disruption is mitigated by the speed of response and the proportion of drivers who divert. However, for certain minor incidents, a VMS induced traffic diversion might increase travelers' delay instead of reducing it