Vehicle Occupants Research Articles

An Electronic Economic Controller for automatically adjusting the intensity of Solar-Powered LED Street Lightning Systems in rural areas

Using photovoltaic energy for street lighting systems is increasing rapidly in rural areas. An economic and smart controller design for automatic intensity control of LED-based street lights is the main challenge. In this work, an electronic-based controller is proposed for controlling the LED light intensity automatically, considering vehicle movement and atmospheric conditions. A HC-SR501 PIR sensor is used for detection of vehicle movements and to assist the controller in making decisions regarding the brightness of the LED. The proposed LED is operated at three different energy levels, such as 0 W, 10 W, and 30 W. In the daytime, the load will remain off, and at night, the LED will operate at two power levels, 33% and 100%, based on the vehicle occupancy rate. In addition, the proposed controller will enhance the battery lifetime. Furthermore, the proposed work ensures significant savings in energy and operating costs and also favors environmental responsibility. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 58-65

Tipping points, multistability, and stochasticity in a two-dimensional traffic network dynamics

Mitigating traffic jams is a critical step for the betterment of the urban transportation system, which comprises a large number of interconnected routes to form an intricate network. To understand distinct features of vehicular traffic flow on a network, a macroscopic two-dimensional traffic network model is proposed incorporating intra-nodal and inter-nodal vehicular interaction. Utilizing the popular techniques of nonlinear dynamics, we investigate the impact of different parameters like occupancy, entry rates, and exit rates of vehicles. The existence of saddle-node, Hopf, homoclinic, Bogdanov–Takens, and cusp bifurcations have been shown using single or biparametric bifurcation diagrams. The occurrences of different multistability (bistability/tristability) phenomena, stochastic switching, and critical transitions are explored in detail. Further, we calculate the possibility of achieving each alternative state using the basin stability metric to characterize multistability. In addition, critical transitions from free flow to congestion are identified at different magnitudes of stochastic fluctuations. The applicability of critical slowing down based generic indicators, e.g., variance, lag-1 autocorrelation, skewness, kurtosis, and conditional heteroskedasticity are investigated to forewarn the critical transition from free flow to traffic congestion. It is demonstrated through the use of simulated data that not all of the measures exhibit sensitivity to rapid phase transitions in traffic flow. Our study reveals that traffic congestion emerges because of either bifurcation or stochasticity. The result provided in this study may serve as a paradigm to understand the qualitative behavior of traffic jams and to explore the tipping mechanisms occurring in transport phenomena.

Peculiarities of determining the capacity of land urban passenger transportation vehicles

Introduction. In the conditions of motorization growth the necessity to improve the quality of passenger service by public transport increases significantly. The determining factor for finding the design capacity of vehicles (TC) is the comfort provided to passengers. On the one hand, in order for public transport to be popular in comparison with private motor transport, it must provide comfortable travel conditions, which are ensured by the provision of free places for sitting and (or) the availability of sufficient space for standing. On the other hand, guaranteed provision of all passengers with seats with a comfortable interval between rows of seats, requires an increase in the number of vehicles to master the same volume of transportation and can lead to a sharp increase in the cost of transportation and fare, at which the economic advantages of public transport over private transport will be less obvious. The aim of the study was to find a compromise calculated occupancy rate of vehicles, at which public transport provides the necessary attractiveness and comfort for passengers and the required carrying capacity.Materials and methods. The methodological basis of the work was general scientific principles and research methods: empirical (expert evaluation) and theoretical (analysis, synthesis, systematization) methods. In order to achieve the set goal, the following tasks were solved: 1. The methods of determining the quality of occupancy quality of the vehicle interior were analyzed. 2. The maximum acceptable capacity of the considered public transportation vehicles was determined. 3. The proposals on the use of the calculated indicator of the density of standing passengers placement in determining the carrying capacity of public transportation are given.Discussion and conclusion. The conducted analysis of manuals on operation, repair, acting in corresponding periods of operation of the chosen vehicles shows that the capacity of vehicles according to norms of manufacturers of vehicles and social standard of the Ministry of Transport does not allow to reach the necessary quality of transportation. The performed research has allowed to establish a comfortable level of occupancy of vehicles for a passenger and a calculated indicator of the density of standing passengers.Practical significance. The established comfortable for passengers occupancy of vehicles can be used in the development and updating of social standards of the Ministry of Transport of the Russian Federation.Originality. There is an assessment of the travel conditions depending on passenger occupancy in different types of public transport vehicles operated in the Russian Federation and the Republic of Belarus.

Daily motor vehicle traffic volume and other risk factors associated with road deaths in U.S. counties

Introduction: Road death risk is often characterized as deaths per volume of traffic in geographic regions, the denominator in miles or kilometers supposedly indicative of the magnitude of risk exposure. This paper reports an examination of the differences in the predictive value of factors hypothesized to influence traffic volume and road death risk. Method: The association of 11 risk factors in U.S. counties during the first 7 months of 2020 was examined for consistency of predictions of road death and traffic volume measured by cell phone and vehicle location data. The study employed least squares regression for traffic volume and Poisson regression for deaths with the population as the offset variable. Results: The directions of the regression coefficients for traffic volume and odds of road deaths per population were opposite from one another for 9 of the 11 variables in the analysis of vehicle occupant deaths. Only the coefficients for maximum daily temperature and Saturday travel were in the same direction. The confidence intervals of three risk ratios for pedestrian deaths indicated low reliability but most of the predictor variables were opposite in association with traffic volume and odds of death. Although traffic volume plunged in the first weeks of the pandemic, the results for the months before and during the COVID-19 pandemic were similar. Practical applications: Traffic volume is an inverse risk factor for road deaths at the local level, likely the result of lower speeds on congested roads. Without the application of countermeasures aimed at reducing speed and other risk factors, the reduction of road congestion is likely to increase deaths.

Comparison of Traumatic Spinal Fracture Patterns Between Motorcyclists and Occupants of Other Nonheavy Motor Vehicles: A Report from the National Spinal Cord and Column Injury Registry of Iran

ObjectivesTo compare traumatic spinal injury patterns between motorcyclists and occupants of other non-heavy motor vehicles using data from the National Spinal Cord and Column Injury Registry of Iran (NSCIR-IR). MethodsAll drivers/riders and passengers of motorcycles, cars, pick-up trucks, and vans registered between January 2017 to July 2023, met the inclusion criteria for the present study. The logistic regression models were used to compare the patterns of vertebral fracture between the two groups. Results1726 spinal fracture patients were identified, 385 (22.3%) motorcyclists and 1341 (77.7%) car occupants with mean ages 33.2±14.3 and 36.1±13.6 years, respectively (P<0.001). Only 45 (11.7%) motorcyclists used helmets, whereas 856 (63.8%) car occupants used seat belts (P<0.001). The average numbers of fractured vertebrae were 3.9±1.4 and 3.7±1.1 among car occupants and motorcyclists, respectively (P=0.004). The proportions of motorcyclists and car occupants with injuries in each spinal region are as follows: lumbar (50.5% of motorcyclists vs. 40.4% of car occupants; P=0.003), thoracic (39.2% vs. 30.9%; P=0.01), cervical (24.3% vs. 37.0%; P<0.001) and sacral (1.3% vs. 7.5%; P<0.001). The AO Spine type C injuries were present in 6.1% of motorcyclists and 10.1% of car occupants (P=0.03). ConclusionMotorcyclists were younger, less educated, had a higher proportion of males, and less commonly used safety devices than car occupants. The most commonly fractured spine region among both groups was the lumbar region. The cervical and sacral vertebrae fractures were significantly more common in car occupants, whereas the thoracic and lumbar vertebrae fractures were significantly more common in motorcyclists.

INVESTIGATION OF THE PURPOSE OF THE TRAVEL IN THE AREA OF UNA – SANA CANTON FOR THE NEEDS OF EXPRESSWAY CONSTRUCTION

&amp;lt;p&amp;gt;In this paper, research data on the purpose of the trip, the origin and destination of the trip, and the occupancy of vehicles for the construction of the expressway were analyzed. All published data are given in tables with percentages, and graphical representation in a GIS environment is used. The research included nine locations in the area of the Una-Sana Canton, which are also displayed in a GIS environment. Research results showed that the preliminary expressway route should go along the Bihać - Cazin - Velika Kladu&amp;amp;scaron;a section. This research can be useful to all subjects involved in the preparation of expressway feasibility studies, local self-government and the like.&amp;lt;/p&amp;gt;

Analysis of the Covid-19 pandemic on preferences of transport modes

The Covid-19 pandemic has affected the entire world and changed many aspects of daily life, including transportation behaviour and preferences. This study examines the situation of transportation behaviour and preferences before and after the Covid-19 pandemic process through a survey study. A total of 471 people participated in the survey, and 30 questions were posed to participants regarding transportation modes preference, daily usage habits, public transportation usage rate, income status, perceived risk level of transportation systems, preferences before and after the pandemic process, and more. The answers were classified, evaluated and statistically analysed. The results showed that users were significantly affected by the Covid-19 process and changed their transportation mode preferences. The use of buses in urban journeys decreased, and the use of private vehicles significantly increased after the Covid-19 process. Hygiene was the key factor in travel, followed by the vehicle occupancy rate, ventilation, fare and travel time factors. The perceived risk levels of public transportation systems were ranked as metrobus, minibus (paratransit), bus, metro, tram and ferry. The study suggests that future transportation system designs should consider user preferences, as well as changes in education and working conditions, and pandemic conditions.

Factors Affecting the Situational Awareness of Armored Vehicle Occupants.

In the field of armored vehicles, up to 70% of accidents are associated with low levels of situational awareness among the occupants, highlighting the importance of situational awareness in improving task performance. In this study, we explored the mechanisms influencing situational awareness by simulating an armored vehicle driving platform with 14 levels of experimentation in terms of five factors: experience, expectations, attention, the cueing channel, and automation. The experimental data included SART and SAGAT questionnaire scores, eye movement indicators, and electrocardiographic and electrodermal signals. Data processing and analysis revealed the following conclusions: (1) Experienced operators have higher levels of situational awareness. (2) Operators with certain expectations have lower levels of situational awareness. (3) Situational awareness levels are negatively correlated with information importance affiliations and the frequency of anomalous information in non-primary tasks. (4) Dual-channel cues lead to higher levels of situational awareness than single-channel cues. (5) Operators' situational awareness is lower at high automation levels.

Rural and Ethnic Disparities in Out-of-hospital Care and Transport Pathways After Road Traffic Trauma in New Zealand.

The out-of-hospital emergency medical service (EMS) care responses and the transport pathways to hospital play a vital role in patient survival following injury and are the first component of a well-functioning, optimised system of trauma care. Despite longstanding challenges in delivering equitable healthcare services in the health system of Aotearoa-New Zealand (NZ), little is known about inequities in EMS-delivered care and transport pathways to hospital-level care. This population-level cohort study on out-of-hospital care, based on national EMS data, included trauma patients <85years in age who were injured in a road traffic crash (RTC). In this study we examined the combined relationship between ethnicity and geographical location of injury in EMS out-of-hospital care and transport pathways following RTCs in Aotearoa-NZ. Analyses were stratified by geographical location of injury (rural and urban) and combined ethnicity-geographical location (rural Māori, rural non-Māori, urban Māori, and urban non-Māori). In a two-year period, there were 746 eligible patients; of these, 692 were transported to hospital. Indigenous Māori comprised 28% (196) of vehicle occupants attended by EMS, while 47% (324) of patients' injuries occurred in a rural location. The EMS transport pathways to hospital for rural patients were slower to reach first hospital (total in slowest tertile of time 44% vs 7%, P ≥ 0.001) and longer to reach definitive care (direct transport, 77% vs 87%, P = 0.001) compared to urban patients. Māori patients injured in a rural location were comparatively less likely than rural non-Māori to be triaged to priority transport pathways (fastest dispatch triage, 92% vs 97%, respectively, P = 0.05); slower to reach first hospital (total in slowest tertile of time, 55% vs 41%, P = 0.02); and had less access to specialist trauma care (reached tertiary trauma hospital, 51% vs 73%, P = 0.02). Among RTC patients attended and transported by EMS in NZ, there was variability in out-of-hospital EMS transport pathways through to specialist trauma care, strongly patterned by location of incident and ethnicity. These findings, mirroring other health disparities for Māori, provide an equity-focused evidence base to guide clinical and policy decision makers to optimize the delivery of EMS care and reduce disparities associated with out-of-hospital EMS care.

A non-stationary bivariate extreme value model to estimate real-time pedestrian crash risk by severity at signalized intersections using artificial intelligence-based video analytics

Vehicle-pedestrian crashes are generally severe due to the vulnerability of pedestrians compared to the occupants of vehicles. However, the estimation of pedestrian crash risk by severity has not been given adequate attention in the field of proactive safety assessments applying traffic conflict techniques. This study proposes a novel analytical framework to estimate real-time pedestrian crash risk by severity at the signal cycle level while incorporating the effect of time-varying exogenous variables. Specifically, the study applies a non-stationary bivariate extreme value model to jointly model the post encroachment time and Delta-V for estimating real-time pedestrian crash risk by severity at individual signal cycles. The proposed framework is tested on 144 h of video data collected from three signalized intersections in Queensland, Australia. The developed bivariate extreme value model has been found to reliably predict severe and non-severe pedestrian crash frequencies compared to the historical crash records of severe and non-severe pedestrian crashes at those signalized intersections. Results suggest that the frequency of pedestrian conflicts per signal cycle and average pedestrian speed in a signal cycle are associated with real-time pedestrian crash risks. In addition, pedestrian conflicts per signal cycle and average vehicle speed per cycle were associated with the interaction severity component of the non-stationary bivariate extreme value model. The proposed proactive estimation of pedestrian crash risk by severity levels can help design time-sensitive countermeasures for vulnerable road users.

Deep transfer learning architecture for suspension system fault diagnosis using spectrogram image and CNN

The suspension system plays a critical role in automobiles, ensuring the safety and comfort of vehicle occupants. However, extended usage, varying road conditions, external forces, and heavy loads can result in damage and faults within the internal components of the suspension system. To mitigate the occurrence of suspension system failures, the development of an effective fault diagnosis system for suspension components becomes imperative. Traditional fault diagnosis techniques often heavily rely on human expertise, which comes with certain limitations. In response, researchers have embraced intelligent fault diagnosis techniques, with transfer learning-based fault diagnosis emerging as a highly effective approach. By leveraging transfer learning, it becomes possible to extract and select fault-specific features for classification purposes. Deep learning-based methods, with their capacity to extract significant features and essential information from raw data, offer notable advantages. Despite these advantages, the implementation of deep learning-based fault diagnosis in suspension systems remains relatively unexplored and limited. In this article, a deep transfer learning architecture specifically designed for fault diagnosis in suspension systems is proposed. The approach involves employing 12 pre-trained networks and tuning them to identify the optimal model for fault diagnosis. Time domain vibration signals obtained from suspension systems under seven fault conditions and one good condition are transformed into spectrogram images. These images are then pre-processed and used as input for the pre-trained networks in fault classification. The results demonstrate that among the 12 pre-trained networks, AlexNet outperforms the others in terms of classification accuracy while requiring the least amount of training time. Therefore, AlexNet network in conjunction with the spectrogram images of time domain vibration signals for applications in suspension system fault diagnosis is highly recommend.

ETSI ITS: A Comprehensive Overview of the Architecture, Challenges and Issues

Abstract: Intelligent Transportation Systems (ITS) have attracted the attention of developing nations because of their potential to enhance mobility and road safety, two issues that have become increasingly pressing in recent years. Information and Communication Technology (ICT) has accentuated every industry, and the automobile industry is no exception, allowing vehicles to communicate among themselves and the surrounding infrastructure for information exchange using Vehicle-to-Everything (V2X) communication. Vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-cloud (V2C) connections are made possible through the use of state-of-the-art networking technology. With EVehicles at the forefront, the vision of an ITS expands further. ITS is anticipated to offer wireless network-based services including vehicle occupant entertainment, traffic flow optimization, and accident prevention. Most nations have been trying to standardize the ITS architecture, and the European Union (EU) has been at the forefront by developing and standardizing the ETSI (European Telecommunications Standards Institute) architecture. As the most mature ITS architecture, this paper thoroughly explains the ETSI architecture in a single document for both the researcher's and newcomers' ease. Additionally, the challenges and issues pertinent to adopting and implementing the ITS ecosystem have also been discussed in detail.

Hospitalization information and burden of pediatric inpatients in transport accidents

BackgroundTransport accidents are one of the leading causes of child morbidity and mortality worldwide and represent a significant public health burden. This study aimed to investigate the hospitalization information and burden of pediatric inpatients in transport accidents in China.MethodsIn this study, we collected the cover page of the medical records of pediatric inpatients in transport accidents using the Futang Research Center of Pediatric Development (FRCPD) database from January 1, 2016 to December 31, 2021. Then, we extracted the epidemiological characteristics, including demographic characteristics, cases distribution, disease information, and hospitalization burden.ResultsAmong 36,455 included inpatients, males, aged 1–3 years, East China, July were dominant in different subgroups. In transport accidents, pedestrians were the most frequently type of injury (65.69%). Of all known lesions, craniocerebral/nerve injury was the more common results in pediatric inpatients in transport accidents (33.93%). In addition to pedal cyclists more susceptible to sport system injury, other types of injured person with transport accidents were mainly craniocerebral/nerve injury. In terms of the type of discharge, occupant of heavy transport vehicle or bus and people with craniocerebral/nerve injury had the highest mortality rate after hospitalization in all type and lesion of injured person groups, respectively. The largest hospitalization burden in the type of injured person was occupant of heavy transport vehicle or bus.ConclusionsThis study revealed that epidemiological characteristics and the main factor influencing the hospitalization information and burden of children with traffic accidents in China.

A DEEP LEARNING POWERED SMART PARKING SYSTEM BASED ON FACIAL RECOGNITION AND LICENSE PLATE ANALYSIS

In today's urban landscapes, parking congestion has become a pervasive challenge, leading to wasted time, increased emissions, and driver frustration. The ever-growing urban population, coupled with the increasing number of vehicles, has led to a severe shortage of parking spaces in metropolitan areas. This scarcity has not only made finding a parking spot a time-consuming and frustrating experience but has also contributed to traffic congestion and environmental pollution. Traditional parking management systems, which often rely on manual ticketing or physical barriers, are becoming inadequate in addressing these challenges. To address this issue, innovative solutions that combine technology and automation are on the rise. Traditional parking management systems often rely on physical infrastructure and human intervention, resulting in inefficiencies and limited scalability. In contrast, the integration of deep learning techniques and advanced computer vision technology into parking management opens up new possibilities for a smarter, more efficient, and user-friendly experience. This system leverages two key components: facial recognition technology to identify vehicle occupants and automatic recognition of license plate numbers for vehicle identification. By seamlessly integrating these technologies, the system not only facilitates effortless parking but also enhances security and optimizes parking space utilization.we will explore the fundamental components, benefits, and potential impact of the Face and Number Plate-Based Smart Parking System. Experimental results shows that improved efficiency in smart parking system using face and number plate verification system.

Vehicle Occupant Detection Based on MM-Wave Radar.

With the continuous development of automotive intelligence, vehicle occupant detection technology has received increasing attention. Despite various types of research in this field, a simple, reliable, and highly private detection method is lacking. This paper proposes a method for vehicle occupant detection using millimeter-wave radar. Specifically, the paper outlines the system design for vehicle occupant detection using millimeter-wave radar. By collecting the raw signals of FMCW radar and applying Range-FFT and DoA estimation algorithms, a range-azimuth heatmap was generated, visually depicting the current status of people inside the vehicle. Furthermore, utilizing the collected range-azimuth heatmap of passengers, this paper integrates the Faster R-CNN deep learning networks with radar signal processing to identify passenger information. Finally, to test the performance of the detection method proposed in this article, an experimental verification was conducted in a car and the results were compared with those of traditional machine learning algorithms. The findings indicated that the method employed in this experiment achieves higher accuracy, reaching approximately 99%.

Risk and Protective Factors for Injury in Adult Front- and Rear-Seated Motor Vehicle Occupants in New York State.

Although seatbelt use is known to reduce motor vehicle occupant crash injury and death, rear-seated adult occupants are less likely to use restraints. This study examines risk and protective factors associated with injury severity in front- and rear-seated adults involved in a motor vehicle crash in New York State. The Crash Outcome Data Evaluation System (CODES) (2016-2017) was used to examine injury severity in front- and rear-seated occupants aged 18 years or older (N = 958,704) involved in a motor vehicle crash. CODES uses probabilistic linkage of New York State hospitalization, emergency department, and police and motorist crash reports. Multivariable logistic regression models with MI analyze employed SAS 9.4. Odds ratios are reported as OR with 95% CI. The mortality rate was approximately 1.5 times higher for rear-seated than front-seated occupants (136.60 vs. 92.45 per 100,000), with rear-seated occupants more frequently unrestrained than front-seated occupants (15.28% vs. 1.70%, p < 0.0001). In adjusted analyses that did not include restraint status, serious injury/death was higher in rear-seated compared to front-seated occupants (OR:1.272, 1.146-1.412), but lower once restraint use was added (OR: 0.851, 0.771-0.939). Unrestrained rear-seated occupants exhibited higher serious injury/death than restrained front-seated occupants. Unrestrained teens aged 18-19 years old exhibit mortality per 100,000 occupants that is more similar to that of the oldest two age groups than to other young and middle-aged adults. Speeding, a drinking driver, and older vehicles were among the independent predictors of serious injury/death. Unrestrained rear-seated adult occupants exhibit higher severe injury/death than restrained front-seated occupants. When restrained, rear-seated occupants are less likely to be seriously injured than restrained front-seated occupants.

Modification of the association between vehicle model year and crash-related injury by race and ethnicity for North Carolinian passenger vehicle occupants.

Modification of the association between vehicle model year and crash-related injury by race and ethnicity for North Carolinian passenger vehicle occupants.

Monitoring the Center of Gravity of a Vehicle Seat to Detect the Occupant Position

Deploying an airbag when a vehicle occupant is too close to it can cause injury. An adaptive Airbag Control Unit (ACU) would improve the effectiveness of the safety system, provided it is aware of the actual position of the occupants once the crash is going to occur. Occupants can be monitored with vision-based and radar-based sensing in the vehicle, but the research question is whether other reliable devices exist. In this research, a real seat is equipped with four sensors in the supports from the floor, as well as an Inertial Measurement Unit (IMU) and a microcontroller. The device is capable of identifying correct position or different Out of Position (OP) conditions and inform an adaptive ACU. The paper presents the seat layout in detail and its testing in extensive driving experiments with multiple participants. Depending on the position of the driver, the identification is correct 45–100% of the time. Monitoring the occupant position by a sensorized seat is feasible and can improve the reliability of the onboard safety system when integrated with other occupant monitoring devices.

Effect of vehicle speed on injury of child passenger in frontal crashes

Speed is major cause of death to vehicle occupants especially children. Studying effect of speed on child injury severity is crucial to establishing safety regulations. This work investigates the effect of vehicle speed on the severity of injury sustained by three-year-old (3YO) children. Restrained 3YO child dummy Finite Element (FE) model was used to conduct crash simulation at impact speed of 40km/h, 46 km/h and 48 km/h in Ford Taurus FE model using LS DYNA code. Vehicle deceleration was found to increase with increase in speed. Head Injury Criteria (HIC36), HIC15, chest acceleration (CA), chest severity index (CSI), chest deflection (CD) and neck moment (NM) were evaluated and compared with National Highway Traffic Safety Administration (NHTSA) threshold. It was found that the injury parameters sustained by 3YO child increased with increase in speed. HIC36 was found to be above threshold for 48km/h and 46km/h. Speed of 40 km/h had all the injury values below the threshold. Chest deflection was lower than the threshold for all the three speeds. Chest acceleration and neck moment were also presented and their values also increase with speed, though, NHTSA doesn’t capture their threshold. The study disseminates information on the effect of speeding on child injuries in frontal crashes for road safety agents and vehicle designers and users. Limiting vehicle speed is therefore crucial to child occupant protection.

Examining Congestion Pricing Scheme Effectiveness Using the Travel Time Congestion Index

This study investigated the effectiveness of congestion pricing (CP) using the travel time congestion index (TTCI), a congestion assessment tool, in Hyderabad, India. Initially, a set of hypothetical mode choice scenarios under the CP scheme were designed to collect car users’ perceptions based on a stated preference (SP) experiment. Based on the SP survey data, discrete travel behavior models were developed to estimate the probable modal trade-off among cars, two-wheelers, and public buses under the generated CP scenarios. Using the existing traffic, geometric, and land-use data from the most congested corridors of the study city, TTCI values were estimated for the base and future conditions, followed by commuter volume estimation for the different conditions using vehicle occupancy factors. Further, the commuter volume derived from modal trade-off under CP scenarios was converted into traffic volume for the identified congested corridors. Finally, the TTCI values were re-estimated using the final traffic volume and compared across worst, best-worst (intermediate), and best case CP scenarios for the base and future years. An annual average growth of 5% in traffic volume was considered. The results show a significant improvement in TTCI values across all identified corridors under CP implementation, indicating its effectiveness toward congestion alleviation. Such demonstration of CP effectiveness could play a major role in making CP a successful travel demand management measure for cities burdened with congestion.