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

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

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

Performance Comparison of Bus Travel Time Prediction Models across Indian Cities

Road traffic congestion has become a global worry in recent years. In many countries congestion is a major factor, causing noticeable loss to both economy and time. The rapid increase in vehicle ownership accompanied by slow growth of infrastructure has resulted in space constraints in almost all major cities in India. To mitigate this issue, authorities have shifted to more sustainable management solutions like Intelligent Transport System (ITS). Advanced Public Transportation System (APTS) is an important area in ITS which could considerably offset the growing ownership of private vehicles as public transport holds a noticeable mode share in several major cities in India. Getting access to real-time information about public transport would certainly attract more users. In this regard, this work aims at developing a reliable structure for predicting arrival/travel time of various public transport systems under heterogeneous traffic conditions existing in India. The data used for the study is collected from three cities—Surat, Mysore, and Chennai. The data is analyzed across space and time to extract patterns which are further utilized in prediction models. The models examined in this paper are k-NN classifier, Kalman Filter and Auto-Regressive Integrated Moving Average (ARIMA) techniques. The performance of each model is evaluated and compared to understand which methods are suitable for different cities with varying characteristics

Real time bus travel time prediction using k-NN classifier

Predicting bus arrival times and travel times are crucial elements to make the public transport more attractive and reliable. The present study explores the use of Intelligent Transportation Systems (ITS) to make public transportation systems more attractive by providing timely and accurate travel time information of transit vehicles. However, for such systems to be successful, the prediction should be accurate, which ultimately depends on the prediction method as well as the input data used. In the present study, to identify significant inputs, a data mining technique, namely k-NN classifying algorithm is used. It is based on the similarity in pattern between the input and historic data. These identified inputs are then used for predicting the travel time using a model-based recursive estimation scheme, based on Kalman filtering. The performance is evaluated and compared with methods based on static inputs, to highlight the improved prediction accuracy

Comparative analysis of travel time prediction algorithms for urban arterials using Wi-Fi Sensor Data

Travel time is one of the elementary traffic stream parameters in both users’ and transport planners’ perspective. Conventional travel time estimation methods have performed out of sorts for Indian urban traffic conditions characterized by heterogeneity in transport modes and lack of lane discipline. Robust to these limitations, Media Access Control (MAC) matching is perceived to be a reliable alternative for travel time estimation. To assist with real-time traffic control strategies, this study aims at developing a reliable structure for forecasting travel time on Indian urban arterials using data from Wi-Fi/ Bluetooth sensors. The data collected on an urban arterial in Chennai has been used as a case study to explain the value of such data and to explore its applicability in implementing various prediction models. To this end, this study examines and compares three different machine learning algorithms k-Nearest Neighbour (kNN), Random Forest (RDF), Naive Bayes, and Kalman filtering technique for prediction. The performance of each model is evaluated to understand its suitability

Proposed Empirical Approach to Measuring Traffic String Stability

This study originated with the intent of qualifying traffic string stability from empirical observations. A new responsiveness angle measure was developed to assess driver instincts under vehicle-following conditions. In this measure, the degree of the follower vehicle's attention towards its leader vehicle's actions is quantified. In understanding string stability in the traffic stream and assessing the propagation of disturbances, the newly conceptualized measure was used along with a discrete Fourier transform to measure the frequencies associated with responsiveness angle sequences. In this transform, a higher frequency of the angle depicts unstable conditions and vice versa. In assessing string stability from the empirical observations, vehicular trajectory data were developed from three study sections. Two study sections tended to have homogeneous lane-wise traffic, whereas the third section had mixed (heterogeneous) traffic. The results of the string stability analysis over the study sections showed that string stability varied with the change in traffic flow conditions, road geometries, and traffic flow type. In the case of free-flow conditions, the traffic streams were found to be stable with marginal disturbances in the responsiveness angle. From the analysis, it was observed that, in the case of study Section 3, around 26 instances of the stream were extremely unstable conditions (frequency equal to 10). For study Sections 1 and 2, the traffic stream was unsteady for 4 and 13 instances, respectively. However, as the traffic flow level rose, string stability deteriorated. This study demonstrated a novel approach to analyzing string stability based on actual traffic conditions that can be implemented in real time for traffic stream monitoring.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin

Investigating Performance of a Novel Safety Measure for Assessing Potential Rear-End Collisions: An Insight Representing a Scenario in Developing Nation

Road safety is one of the major concerns in the ever-growing traffic network. In addressing this, surrogate safety measures play a critical role in identifying collision instincts. Besides the added advantage of quantifying collision instincts in advance, surrogate safety measures have their limitations. For example, in some instances, those measures tend to show erroneous results. In this paper, a new surrogate safety measure Instant Heeding Time (IHT), is presented based on follower vehicle attention in the traffic streams. This new measure is integrated with a distance gap and the vehicles' speeds to assess probable rear-end collisions. Further, along with other safety measures, the developed safety framework is tested over a study section, with the help of trajectory datasets at three traffic flow conditions (free flow, capacity, and congested) under prevailing heterogeneous (mixed) traffic conditions. Based on the safety framework, it is observed that, in the case of free flow and capacity conditions, 23 and 55 probable rear-end collisions points are detected. At the congested conditions, no rear-end collision points are observed. Further, smaller vehicles in the traffic stream are associated with a higher number of rear-end collision instincts than other vehicle categories. The conceptualized safety framework can be applied on a real-time basis for monitoring the safety measures for vehicles in a mixed traffic stream.Transport and Plannin

Comparative Evaluation of Density Estimation Methods on Different Uninterrupted Roadway Facilities: Few Case Studies in India

Traffic density is one of the fundamental macroscopic characteristics and it is of prime importance when assessment of a facility has to be done based on both users as well as planner’s perspective. According to the Highway Capacity Manual (HCM), USA, for expressways and multilane roads, the Level-of-Service (LoS) has been defined taking density as the governing factor. Density is treated as the fundamental macroscopic spatial parameter of traffic flow, as it directly indicates the quality of traffic and ease with which one can drive. The present research study focuses on applicability of density estimation methods on multi-class, heterogeneous, non-lane based Indian traffic condition. In this research study, mid-block road sections namely, on Delhi-Gurgaon Expressway, Ahmedabad-Vadodara Expressway, National Highway (NH)-8 and urban arterial road in Chennai are considered. The expressways are the highest class of roads in Indian road network with different roadway, traffic conditions and with access control facilities. Delhi–Gurgoan Expressway is eight-lane divided facility and Ahemdabad –Vadodara Expressway is four-lane divided with 2.6 m paved shoulder. On the other hand, selected section on National Highway (NH8) is four-lane divided road. Real traffic data was collected through video-graphic survey on all these roads with different traffic conditions. HCVs (Heavy Commercial Vehicles) and MCVs (Multi-axle Commercial Vehicles) were the major traffic composition in congested regime. Thus, the present study focuses on the comparison of density estimation methods on different roadway and traffic conditions. The three methods to be employed for the purpose of estimating traffic density on the study section are: (1) using traffic flow fundamental equation relating speed, flow and density, HCM definition (2) using Cumulative input–output Plots (Input–Output method) (3) Eddie’s (x, t) method. This paper aims to empirically quantify the difference in the density estimation based on the aforementioned methods. Assuming input–output provides theoretical density, the errors in the estimation of density using fundamental equation under different traffic flow conditions are also quantified. In spite of growing body of literature disputing about the effectiveness and applicability of various density estimation methods, the key finding from this research indicates that all three abovementioned methods works very well under uncongested traffic flow condition. However, for oversaturated traffic conditions the density estimation using fundamental relationship has errors, primarily due to errors in estimation of the space mean speed since the vehicles which persisted within trap length for period longer than time-interval under consideration are not incorporated in the calculation, which is not the case for other two methods. Moreover, the research study concludes that smaller trap length (<100 m) can have errors in estimation of density values as compared to actual density values. The findings are useful for condition assessment of traffic flow for design and operation purposes