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This study emphasises the criticality of delay as a performance metric for signalized intersections and the challenges associated with its estimation, particularly in the context of Multi-class and Lane-free (MCLF) traffic conditions. Traditional delay models are often inadequate for such conditions, necessitating the development of a tailored approach. A novel delay equation is proposed, integrating insights from queuing theory principles with consideration of multi-class of vehicles and lane-free movement. Key features include assumption of random arrival and departure pattern as well as distribution, incorporation of Passenger Car Equivalent (PCE) and virtual lane concepts to account for the diverse vehicle classes and lane-free movement prevalent in Indian traffic. The model’s efficacy is demonstrated through comparison with conventional in practice delay models, showing its superior performance. This tailored approach enhances the accuracy of delay estimation and also highlights the importance of accounting for specific traffic characteristics in optimising signal design for intersections under MCLF traffic conditions.

Scientific literature has overlooked how PM 2.5 concentrations vary with varying pedestrian heights near a roadway. Understanding this is important because walking is an essential commuting element of a sustainable transportation system, and pedestrians’ height varies widely. Therefore, the focus of the current study is to bridge this gap using results from CALINE 4 model and mobile PM 2.5 measurements. In CALINE 4, a simple pedestrian pathway depicting the selected study site located near the Sardar Patel Road, Chennai, India, was simulated. The PM 2.5 concentrations were estimated on this pathway at varying heights (0.1−1.8 m) in 135 simulated runs. Subsequently, the sensitivity of the PM 2.5 exposure difference across heights was explored with varying ambient PM 2.5 concentrations, wind speed, traffic volume, and traffic compositions. Results indicated that the PM 2.5 concentrations reduced with increasing heights of pedestrians in all the modelled runs. When this PM 2.5 exposure difference was investigated with varying surrounding conditions, it was found that the difference in PM 2.5 exposure across heights was influenced by the wind speed, traffic volume, and traffic composition. Ambient PM 2.5 concentrations had no discernible effect on it. Car-dominated traffic with a higher mode share of heavy commercial vehicles was marked with the highest PM 2.5 exposure difference across heights. For traffic volume, it was observed that for every 100 vehicles hr −1 increase in traffic volume, the PM 2.5 exposure difference increased by 0.13 µg m −3 m −1 in the range of pedestrian’s height. For wind speed, calculations suggested that for every 1 m s −1 increase in wind speed, the PM 2.5 exposure difference was reduced by 0.095 µg m −3 m −1 in the range of pedestrian’s height. Finally, to bolster the modelling results, mobile PM 2.5 measurements (using portable, low-cost optical particle sensors) were conducted near a busy urban roadway at two different heights, 80 cm and 150 cm, during peak and off-peak hours. The results of mobile measurements were found to be consistent with CALINE 4 modelled results.

Urbanization and gro wing demand for travel, causes the traffic system to work ineffectively in most urban areas leadin g to traffic congestion. Many approaches have been adopted to address this problem, one among them being the signal co-ordination. This can be achieved if the platoon of vehicles that gets discharged at one signal gets green at consecutive signals with minimal delay. However, platoons tend to get dispersed as they travel and this dispersion phenomenon should be taken into account for effective signal coordination. Reported studies in this area are from the homogeneous and lane disciplined traffic conditions. This paper analyse the platoon dispersion characteristics under heterogeneous and lane-less traffic conditions. Out of the various modeling techniques reported, the approach based on diffusion theory is used in this study. The diffusion theory based models so far assumed thedata to follow normal distribution. However, in the present study, the data was found to follow lognormal distribution and hence the implementation was carried out using lognormal distribution. The parameters of lognormal distribution were calibrated for the study condition. For comparison purpose, normal distribution was also calibrated and the results were evaluated. It was foun d that model with log normal distribution performed better in all cases than the o ne with normal distribution.

With recent advances in autonomous vehicles and traffic monitoring systems, the use of light detection and ranging (LIDAR) is becoming more popular. One of the essential components of these systems is a LIDAR point-cloud classifier. This work introduces a generalized classification framework based on traditional 3D point cloud processing algorithms, together with a classification model with interpretable inputs. The framework consists of three stages, wherein the first two stages involve the development of input features of the classification model through preprocessing and feature generation algorithms. In the final stage, the multiclass machine learning (ML) model predicts the vehicle class. The study also presents the refining of data from off-line techniques to improve the performance of the ML model. The framework is validated using real-world LIDAR data that represent heterogeneous laneless traffic. A comparison of a range of point-cloud ground segmentation and clustering algorithms is conducted on this data set, and it is shown that density-based spatial clustering of applications with noise (DBSCAN) and ground segmentation by m-estimator sample consensus (MSAC) give the best clustering output. Seven features representing the dimension, distribution, and density of the clusters were extracted using bounding-box fitting and line-fitting algorithms. After training with various classification models using these features, the adaptive boosting algorithm (AdaBoost) was determined to have the highest accuracy (0.922 F1 score) for five output classes. Furthermore, it is demonstrated that this accuracy can be enhanced by data-refining techniques such as skewness reduction and region-of-interest boundary selection. The final model obtained has an accuracy of 98.4% (0.969 F1 score). The results show that the framework is well-suited for applications that employ multiclass classifiers for heterogeneous and laneless traffic.

Traffic density is an indicator of congestion and the present study explores the use of data-driven techniques for real time estimation and prediction of traffic density. Data-driven techniques require large database, which can be achieved only with the help of automated sensors. However, the available automated sensors developed for western traffic may not work for heterogeneous and lane-less traffic. Hence, the performance of available automated sensors was evaluated first to identify the best inputs to be used for the chosen application. Using the selected data, implementation was carried out and the results obtained were promising, indicating the possibility of using the proposed methodology for real time traveller information under such traffic conditions.

With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is one of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and prediction of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of these stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. (1) Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. (2) A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. (3) Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector data is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results.