Bhargava Rama Chilukuri

The paper presents a model-based demand-responsive traffic control system for mixed traffic conditions using sample travel time data. The model incorporates mixed traffic characteristics such as heterogeneity, limited lane discipline of varied vehicle types, and spatio-temporal traffic dynamics across the width of the road. The methodology includes optimization of intersection performance by accommodating the varying traffic demand through signal timing variables. On validation, the model yielded reliable queue estimates within a close proximity of the actual, ranging from 20 to 40 meters. Upon optimization, the proposed model reduced total intersection delay by 15.42% on an average across 14 cycles, for near-saturated traffic conditions. The optimal green splits are found to be responsive to the varying traffic demand. The proposed system is simple and can be easily implemented in the mixed traffic conditions.

Departure Time Planner (DTP) helps to efficiently manage the commute plan by providing smart travel assistance which suggests a departure from a given origin to a destination, given the desired arrival time at the destination. Towards this end, a DTP system is developed using a quasi-connected vehicle system where traffic data is collected from sparse sensor infrastructure. Novel methods and algorithms were developed accounting for the heterogeneous traffic conditions found in India. The traffic state prediction methodology based on the second-order traffic flow model shows that the system can reliably estimate the departure time for Indian conditions.

In the absence of pharmaceutical interventions for the Novel Coronavirus (COVID-19), countries have taken drastic steps like quarantine, prohibit large-scale gatherings, limited transport, social distancing, curfews, and lockdowns to curtail the spread of the virus. In light of these events, the current study attempts to understand the short-term changes in road traffic patterns, using data from two Wi-Fi MAC Scanners deployed at strategic locations in Chennai, India. The results indicate that the road traffic activities significantly reduced due to the restrictions in non-essential trips, workplace suspensions, and strict surveillance during lockdowns. However, as the lockdown rules eased, the road traffic activities began to recover. It is found that complete closedown is most effective in reducing road travel activity, but ad-hoc short duration complete closedowns may only yield temporary benefits. Also, extended lockdowns without proper enforcement may be ineffective since the public appeared to ignore the advisory after a while.

Traffic in developing countries has a heterogeneous vehicular mix that uses all the network links and lacks lane discipline. Modeling and controlling such a mixed traffic system are challenging since most of the well-established models were developed for homogeneous traffic. It is hypothesized that segregating the mixed traffic by assigning a unique mode to each link will enhance system capacity. Towards achieving it, this paper proposes optimal mode-route assignment formulations with the objective of minimizing the total system travel time. However, perfect segregation is not always possible since the solution depends on the network topography. A viable solution is to make some links multi-modal. Another formulation is also presented in this paper to address this issue. Both the formulations are demonstrated using sample networks. Linear and nonlinear integer mathematical programming methods are used to explore the qualitative characteristics of optimal mode-route assignment using the single-path routing method. The results indicate that, in the worst case where perfect segregation is not possible, proposed formulation II can identify network with the least number of the multi-modal links. This research will help to develop effective strategies to model, control, and enhance the safety of mixed traffic networks.

Link cost function and link capacity are critical factors in traffic assignment modeling. Popular link cost functions like the Bureau of Public Roads (BPR) function have well-known drawbacks and are not suitable for mixed traffic conditions where a variety of vehicle classes use the road in a non-lane-based movement. Similarly, capacity is generally considered as a constant value. However, in mixed traffic conditions, capacity is not constant, but a function of vehicle class composition. Toward addressing these issues, this paper proposes a link cost function in relation to link travel time and link capacity in relation to vehicular traffic flow for mixed traffic conditions. The functions are developed based on the kinematic wave model, which is popularly used for estimating traffic dynamics on the roads. The developed link cost function and link capacity use field measurable parameters that incorporate mixed traffic features. The functions are validated against empirical data obtained from 12 signal cycles from two different signalized intersections in Chennai, India, representing different scenarios of mixed traffic, and it was found that the results match well with the empirical data.

A thorough understanding of the fundamental relation of traffic flow variables is critical for the efficient operation of traffic systems. However, their relationships in mixed traffic are challenging to model due to the continuously changing vehicle composition. This paper proposes a composition-based approach for estimating the fundamental relationships between traffic flow variables using empirical data. The methodology seeks to eliminate the difficulties in class-specific ss identification by introducing a continuous wavelet transformation with oblique cumulative arrival and oblique occupancy time plots. We used machine learning (ML) algorithms to delineate regimes and showed the fundamental diagrams for a given location that has a composition-invariant free-flow branch but has distinct composition-specific branches in congestion. Also, it was observed that the congested regime (CR) has a wide scatter indicating possible stochastic inter-class interactions for varying vehicular composition. We proposed a distance optimization method to re-cluster the CR data and found that the proposed method improves the fit with the empirical observations. The inter-class interactions result illustrates that the heavy vehicles will dominate the high-speed vehicles with the increase of AO. It is found that beyond a critical level of AO in congestion, all vehicle class travel at the same speed. Finally, it is found that validation with different datasets shows that the proposed methodology is robust in estimating fundamental diagrams under mixed traffic conditions.

Restriction of the movement of heavy-trucks in a transportation network is a commonly used strategy to mitigate traffic congestion and pollution issues, especially in congested urban areas. However, the cost and capacity functions used in the literature are inadequate to describe the traffic dynamics in heterogeneous conditions. This paper proposes a systematic framework with new cost and capacity functions, to identify critical links in a network and to determine the minimum cost truck routes in the network by restricting trucks on identified critical links. A hierarchical mathematical programming framework with integer programming formulations are presented. The capacity and cost functions are derived based on a multiclass model and shockwave analysis to represent realistic traffic flow interactions between the trucks and cars. The model results are validated using VISSIM simulation, which shows that the total delay reduced and the network capacity utilization improved with truck restriction.

Major cities worldwide are plagued by severe traffic congestion during peak periods. Traffic routing to utilise unused link capacities is a good strategy to reduce congestion and increase network performance. A multi-objective optimization model that seeks a via-media solution between System Optimal (SO) and User Equilibrium (UE) network flows while ensuring better utilization of the excess capacities available in the network is proposed. The number of paths between the O-D pairs is restricted to eliminate paths with long travel times and a constraint on Total System Travel Time (TSTT) is introduced. The proposed problem is solved using the weighted average method to determine Pareto optimal solutions. It is found that restricting the flow to only four paths resulted in a TSTT value that is only marginally higher (1.4%) than the SO model. When evaluated against the UE and SO models, the proposed model significantly improved the link capacity utilization (up to 73%) on some of the links. Additionally, the travel times improved by up to 30% on some paths, compared to the UE model. Although certain paths have longer travel times, the TSTT of Pareto front points is less than or equivalent to the UE model. Thus, the model yields a better distribution of flow in the network and is found to be superior to the existing models. Such a model can potentially contribute to sustainability, safety, and better management of transportation infrastructure.

Increased travel times are often observed on urban roads, with signalized intersections being the major bottlenecks. The inability of existing static signal timings in accommodating the actual demand fluctuations could be one of the contributing factors. A traffic-responsive signal control system that changes signal timings according to traffic volume fluctuations may alleviate this problem. However, such problems are conventionally formulated based on the data collected from location-based sensors, which are infrastructure intensive and costly and fail to capture mixed and disordered traffic conditions. Considering these limitations, this paper presents an optimal signal design using sample travel time information collected from mobile data sources such as GPS/Bluetooth/Wi-Fi sensors that work independently of the traffic conditions and are relatively cost-effective. The proposed adaptive signal design minimizes total intersection delay at isolated intersections for every cycle based on the traffic conditions observed in the previous cycle. The mathematical programming-based formulation uses shock waves formed during the red and green phases to estimate optimal-phase durations. Results revealed that the proposed design is capable of handling traffic flow fluctuations without requiring the entire traffic stream data. The system demonstrated that sample data from four probe vehicles per phase is adequate for real-time optimal signal design. Results showed that the proposed model outperformed the existing Webster's signal design procedure with a delay reduction of 11.78% when compared theoretically and 10.41% when implemented in VISSIM.

Traffic infrastructure segmentation is widely used in for digitization of urban areas for smart city applications. In this paper, we use transfer learning for carriageway edge detection for Indian urban roads where lane edge markings are often invisible. Also, the edge of the pavement often overlaps with unpaved shoulders posing challenges for image-based edge detection. The images obtained from various routes in Chennai, India are annotated and their masks were trained using DeepLabV3with ResNet101 and MobileNetV3 as backbones to predict the classes for each pixel. Two performance metrics, AUROC and F1 score, were used to evaluate the methods. Results show that DeepLabV3 with ResNet101 outperformed DeepLabV3 with MobileNetV3.