Microsimulation study of vehicular interactions in heterogeneous traffic flow on intercity roads

Study of the basic traffic flow characteristics and comprehensive understanding of vehicular interaction are the pre-requisites for highway capacity and level of service analyses and formulation of effective traffic regulation and control measures. This is better done by modeling the system, which will enable the study of the influencing factors over a wide range. Computer simulation has emerged as an effective technique for modeling traffic flow due to its capability to account for the randomness related to traffic. This paper is concerned with application of a simulation model of heterogeneous traffic flow, named HETEROSIM, to study the relationships between traffic flow variables such as traffic volume and speed. Further, the model is also applied to quantify the vehicular interaction in terms of Passenger Car Equivalent (PCE) or Passenger Car Unit (PCU), taking a stretch of an intercity road in India as the case for the study. The results of the study, provides an insight into the complexity of the vehicular interaction in heterogeneous traffic

Examining queue-jumping phenomenon in heterogeneous traffic stream at signalized intersection using UAV-based data

© 2020, Springer-Verlag London Ltd., part of Springer Nature. This research presents an in-depth microscopic analysis of heterogeneous and undisciplined traffic at the signalized intersection. Traffic data extracted from the video recorded using an unmanned aerial vehicle (UAV) at an approach of a signalized intersection is analyzed to study the within green time dynamics of traffic flow. Various parameters of Wiedemann 74, Wiedemann 99, and lateral behavior models used in microscopic traffic simulation package, Vissim, are calibrated for the local heterogeneous traffic. This research is aimed at exploring the queue-jumping phenomenon of motorbikes at signalized intersections and its impact on the saturation flow rate, travel time, and delay. The study of within green time flow dynamics shows that the flow of traffic within green time is not uniform. Surprisingly, the results indicate that the traffic flow for the first few seconds of the green time is significantly higher than the remaining period of green time, which shows a contradiction to the fact that traffic flow for the first few seconds is lower due to accelerating vehicles. Mode-wise traffic counted per second shows that this anomaly is attributed to the presence of motorbikes in front of the queue. Consequently, the outputs of simulation results obtained from calibrated Vissim show that the simulated travel time for motorbikes is significantly lower than the field-observed travel times even though the average simulated traffic flow matches accurately with the field-observed traffic flow. The findings of this research highlight the need to incorporate the queue-jumping behavior of motorbikes in the microsimulation packages to enhance their capability to model heterogeneous and undisciplined traffic

Inhibition of glucose metabolism selectively targets autoreactive follicular helper T cells.

Follicular helper T (TFH) cells are expanded in systemic lupus erythematosus, where they are required to produce high affinity autoantibodies. Eliminating TFH cells would, however compromise the production of protective antibodies against viral and bacterial pathogens. Here we show that inhibiting glucose metabolism results in a drastic reduction of the frequency and number of TFH cells in lupus-prone mice. However, this inhibition has little effect on the production of T-cell-dependent antibodies following immunization with an exogenous antigen or on the frequency of virus-specific TFH cells induced by infection with influenza. In contrast, glutaminolysis inhibition reduces both immunization-induced and autoimmune TFH cells and humoral responses. Solute transporter gene signature suggests different glucose and amino acid fluxes between autoimmune TFH cells and exogenous antigen-specific TFH cells. Thus, blocking glucose metabolism may provide an effective therapeutic approach to treat systemic autoimmunity by eliminating autoreactive TFH cells while preserving protective immunity against pathogens

Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning

The article describes modeling vehicular movements using supervised machine learning algorithms with trajectory data from heterogeneous non-lane-based traffic conditions. The trajectory data on the mid-block road section of around 540 m is used in the study. Supervised machine learning algorithms are employed to model the vehicular positions. A set of parameters were identified for modeling the longitudinal and lateral positions. With the set of parameters, the algorithm’s potentiality for mimicking vehicular positions is evaluated. It was identified that supervised machine learning algorithms would model the vehicles’ positions with accuracy in the range of 20–60 mean absolute percentage error. The k-NN algorithm was marginally edging past all algorithms and acted as a promising candidate for modeling vehicular positions

Modeling of Traffic Flow on Indian Expressways using Simulation Technique

AbstractExpressways in India are vastly different from other roads of the country as bicycles, two-wheelers, three-wheelers and bullock carts are not allowed to ply on these facilities and the traffic essentially consists of cars and trucks. Nevertheless, there is not much research literature specific to these categories of roads. Hence, this work aims to model traffic flow on Indian Expressways by evaluating Passenger Car Unit (PCU) or Passenger Car Equivalents (PCE) of different vehicle categories at different volume levels in a level terrain using the micro-simulation model, VISSIM. This work also aims to evaluate capacity of expressways and to study the effect of vehicle composition on PCU values. It has been found that PCU decreases with increase in volume-capacity ratio irrespective of vehicle category. The study also revealed that at a given volume level, the PCU of a given vehicle category decreases when its own proportion in the stream increases

Traffic characteristics study through processing of video image for expressway in india

India has the third-largest road network in the world. Expressways are the highest class of roads in the Indian Road Network. India’s progress in the road sector measure 600 km of expressways approximately and is planning for achieving more than 15000km of expressway by 2021. Expressways are a controlled-access highway designed for fast traffic, with controlled entrance and exit. Expressways are vastly different from other roads of the country as vehicles such as bicycles, two-wheelers, three-wheelers and bullock carts are not allowed on these roads and additionally, there is no strict lane discipline. A number of research papers are available on studies on traffic stream characteristics for various roadway and traffic conditions. However, very limited studies are carried out on expressways in India Video graphic data collection is widely used in traffic engineering, as video recordings can act as a more detailed, complete, accurate and reliable observational technique. To figure out the exact relationship between the traffic parameters, lots of research has been done over the past. Many attempts have been made earlier for data retrieval from videos. But, there have been very few attempts to discuss the methodology for data extraction and analysis from video for expressway data in India. This paper discusses about video graphic data collection, extraction and analysis of traffic stream characteristics for a duration of 8 hours by taking Ahmedabad -Vadodara expressway (four lane divided carriage way) as a case study. Data has been collected using video graphic survey by fixing high resolution cameras on road over bridge (ROB) such that traffic flow faces the camera. Data on traffic volume, vehicle composition, speed of different vehicle categories, lane utilization etc. are manually extracted from the recorded video after converting it into frames. Data extraction and analysis has been done with the help of the softwares such as Ulead Video Studio, Irfan View and SPSS, MS Excel. The analysis on degree of lane discipline showed that, 97% of vehicles are following lanes. The results also showed that median side lane had high speed traffic as compared to other lane, which may be due to very high car composition (90%)

Effect of bus-lane usage by private vehicles on modal shift

Developing countries urgently need to encourage the use of public transport. With this objective, in May 2013, the government of India implemented a bus rapid transit system (BRTS) with an exclusive bus lane in a rapidly growing city, Indore. However, after 6 months of successful BRTS service, the judicial system ordered that passenger cars should be allowed in the exclusive bus rapid transit lane; this unique decision motivated the present study. The objective is to assess the impact of BRTS service on modal shift before and after the introduction of private vehicles to the exclusive bus rapid transit lane. For these two cases, separate models are formulated and compared using the binary logistic method (BLM) and the artificial neural network (ANN) method. Data on demographic and socio-economic attributes (gender, age and occupation) and trip-related attributes (travel time details and cost saving per day) are collected using a revealed preference survey. An en-route on-board survey is conducted on passengers using buses along the study corridor. Owing to the introduction of vehicles in the exclusive bus rapid transit lane, the probability of passengers switching to the BRTS is observed to decrease from 64·7% to 45·7%. Moreover, ANN provides more accurate results than the BLM in both situations.</p