Browsing by Author "Maripini, Himabindu"

Now showing 1 - 9 of 9

Analysis and use of Wi-Fi data for signal state identification
(Elsevier, 2020) Maripini, Himabindu
Efficient planning, operation and management of transportation facilities require extensive data regarding the traffic demand, patterns and conditions prevalent in the transportation network. Conventional data collection techniques such as loop detectors bear practical limitations such as limited accuracy and applicability, especially in mixed traffic conditions. Since use of smart phones has gained prominence in the recent times, crowd sourced data using Bluetooth and Wi-Fi technologies is perceived to be a reliable alternative for traffic data collection. This eases the rigorous data collection process by considerably reducing the investments on labor, time and other resources. Significant research has been carried out in the extraction and analysis of traffic data from Bluetooth sensors. Changes in privacy settings of smartphones has necessitated the devices to be put on “Discoverable” mode for passive data collection thereby resulting in drastic drops in market penetration rates. Unlike their Bluetooth counterparts, Wi-Fi protocol just requires the Wi-Fi to be switched on for passive data collection, thus resulting in higher penetration rates. This paper presents a preliminary analysis of data extracted from Wi-Fi sensors and the use of it for extracting the signal state information.
Integrating road network operations planning into real-time traffic management: a conceptual framework
(Elsevier, 2025-07) Maripini, Himabindu
Management of road networks is an ever-evolving challenge, particularly as urbanization and mobility demands grow. Effectively addressing this challenge involves integrating high-level directions from various strategies, policies, and plans into network operating plans and real-time traffic control operations through a comprehensive network management system. In collaboration with the Queensland Department of Transportation and Main Roads (TMR), this study introduces a new framework for performance-based, multimodal network management. It aims to enhance the alignment between daily operations and strategic objectives by offering a systematic approach for translating high-level intents into real-time traffic management. In addition, this is supported by proposing a conceptual interface design to support the complete implementation of the framework, including performance visualization and operational recommendations, thereby addressing current shortcomings in existing frameworks and management practices and offering a robust solution for evolving network needs.
Optimal signal control design for isolated intersections using sample travel-time data
(Wiley, 2022-06) Maripini, Himabindu
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.
Optimizing traffic signals for non-uniform arrivals using sparse probe data
(IEEE, 2024-09) Maripini, Himabindu
With the field of traffic signal control progressing towards more adaptive, and computationally efficient models, the integration of sparse travel time data and advanced optimization techniques enhances the intersection performance. This paper presents a novel optimization framework for traffic control using sparse travel time data, with a field penetration rate as low as 10%. The paper focuses on developing mathematical models that can handle the variability in arrival rates both within and across signal cycles. By utilizing delay polygons, we accurately model and minimize the total intersection delay caused by non-uniform vehicle arrivals and signals, leading to optimal signal timing solutions. The model effectively prioritizes queue dissipation for highly saturated phases while minimizing overall intersection delay. The algorithm accommodates variations in phase-wise delays across cycles, indirectly reflecting changes in traffic demand. Additionally, the sample-based design demonstrates performance comparable to volume-based dynamic design in terms of average delay, average speed, and total travel time across cycles. With the innovative use of sample re-identification data obtained through various sensor technologies, the proposed algorithm is capable of delivering optimal control of time varying traffic demand with minimal data input.
A probe-based demand responsive signal control for isolated intersections under mixed traffic conditions
(Taylor & Francis, 2023-01) Maripini, Himabindu
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.
Queue dissipation-based Max pressure signal control using vehicle re-identification data
(IEEE, 2025-09) Maripini, Himabindu
Traffic congestion is a persistent urban challenge, and signal control strategies play a crucial role in enhancing network efficiency. Traditional max pressure-based control systems prioritize optimizing throughput based on known queue lengths but often overlook the impact of intersection delays and real-world operational constraints. This study aims to develop a decentralized max-pressure-based traffic signal control system that integrates both queue length and delay effects within the max-pressure framework by introducing a novel performance metric called queue dissipation time into the control strategy. The proposed cyclic-based max pressure algorithm relies on sample travel time data obtained from probe data sources. It considers pre-defined phase sequences to enhance real-world applicability. When evaluated in a simulation environment with a realistic 20% vehicle re-identification penetration rate, the proposed approach achieved queue and delay performance comparable to control strategies relying on complete vehicle arrival data. Furthermore, implementing dynamic cycle length led to a 23% reduction in delay compared to the fixed-cycle max pressure method, inferring the benefits of adaptive timing adjustments. By incorporating queue dissipation time and considering the constraints of real-world signal operations, the proposed control system enhances the practicality of max pressure-based traffic signal strategies. This study provides a foundation for implementing decentralized adaptive traffic signal control in urban networks, particularly in environments with limited vehicle data availability. The algorithm exhibited reliable performance across various levels of data availability, delivering comparable results under both low (20%) and high (50%) penetration rate scenarios. This consistency highlights its adaptability and operational feasibility, without requiring excessive traffic data or extensive infrastructure.
Simulation-based optimization for heterogeneous traffic control
(Springer, 2022-03) Maripini, Himabindu
One of the major reasons for frequent bottlenecks at intersections is the operation of poorly designed signals with static timings, irrespective of the variations in traffic flow. The fixed signal timings are calculated using conventional design methodologies such as Webster’s and HCM signal design methodologies that are primarily valid for lane disciplined homogenous traffic conditions. Implementation of such design procedures under mixed traffic conditions may not yield the best performance. Therefore, the present study aims to develop a simulation-based optimization that optimizes the performance of the intersection. Spatial performance measure such as travel time is perceived by the users and operators alike and therefore considered for minimization. A simulation-based optimization is performed by implementing various derivative-free optimization algorithms such as Nelder–Mead simplex algorithm and COBYLA using a microsimulation software, VISSIM. The obtained results are better when compared with that of traditional Webster’s design.
A study of delay estimation methods at signalized intersections for mixed traffic condition
(Springer, 2021-03) Maripini, Himabindu
An effective traffic control measure is the one that can significantly reduce delays incurred due to rising traffic congestion and improves travel time reliability. To achieve this, an accurate estimation of delay is very critical. The method of delay estimation varies with the type of data source available, and the type of data collected. This paper aims at studying various methods of delay estimation at an intersection for data types from three data sources—location-based data (videography), Wi-Fi sensor data, and GPS based probe data. The challenges associated with executing each of these methods are also discussed. Besides, a scalable and reliable data source among the three was chosen to calibrate and validate the delay equation suggested by Highway Capacity Manual 2000 (HCM 2000) to suit Indian traffic conditions. A linear regression model was fitted for the progression factor (PF) of the HCM delay equation with an R squared value of 0.85. Validation of the calibrated model yielded an average Mean Absolute Percentage Error (MAPE) of 12.59%. The calibrated model can be used for the estimation of delay based on historic traffic arrival patterns and signal timings.
Traffic state estimation near signalized intersections
(ASCE, 2023-02) Maripini, Himabindu
The primary goal with which any transportation system is designed is to make efficient use of the available infrastructure to achieve better level of service (LoS). However, LoS is observed to be deteriorating on urban roads, especially near signalized intersections, primarily due to the suboptimal operation of traffic signals. To achieve optimal performance of traffic signals, knowledge about the traffic states prevalent in the vicinity of the intersection is essential. Traffic states in general can be estimated at both macroscopic and microscopic level by employing various mathematical and data-driven approaches. However, obtaining these variables near the intersection is difficult and challenging under varying traffic conditions. This paper presents a systematic review of the state of the art in traffic state estimation (TSE) near signalized intersections both under homogeneous, lane-based, and heterogeneous less lane disciplined (HLLD) traffic conditions. This is expected to be a guide to traffic engineers, decision makers, and researchers aiming to gain pertinent knowledge about the sensors that can be used, data that needs to be collected, estimation methods that are suitable, and the intersection performance measures that need to be evaluated. The gaps in the current state of the art and future research directions are highlighted. In addition, insights on ways to address challenges pertaining to TSE near intersections under HLLD traffic conditions are also discussed.