The transportation sector is a major contributor to global emissions, making shifts to mass transit and low-emission powertrains essential. This study conducts a Life Cycle Assessment (LCA) of diesel, Battery Electric Buses (BEBs), and Hydrogen Fuel Cell Electric Buses (HFCEBs) using two approaches: Aggregate Emission-based LCA (AE-LCA), based on average fuel consumption, and Real-world Driving Cycle LCA (RDC-LCA), using GPS-derived driving cycles with second-by-second emissions estimated using CMEM and COPERT 5.6. Across all powertrains and energy pathways, RDC-LCA estimated higher Global Warming Potentials (GWPs) than AE-LCA, with deviations ranging from 8.66% for BEBs charged using captive renewable energy to 36.48% for diesel buses. HFCEBs supplied with green hydrogen achieved up to 85.50% lower GWP than diesel buses, while BEBs exhibited the lowest GWP under decarbonized electricity pathways, including renewable energy and nuclear power. These findings highlight the importance of considering vehicle powertrains and upstream energy sources to minimize transport-sector GWP.

In India, motorized two-wheeler (TW) riders account for 44.5% of fatal road crashes. While factors affecting drivers have been studied, research on pillion riders' injury severity remains limited. The study aims to identify factors causing severe injuries to pillion riders by developing an accurate prediction model. The study includes machine learning (ML) models, such as conditional inference tree, random forest (RF), gradient boosting, support vector machine, and a statistical model ordered probit for comparison. The study accounts for the imbalance in injury severity crash data by adopting data balancing techniques. Also, it recommends a combination of ML techniques, variable importance charts, and individual conditional expectation plots for identifying key variables and their effects. The finding suggests that RF trained in up-sampled data performs better than the remaining models. The presence of a central divider on the road reduces fatal injuries to pillion riders. The likelihood of getting severe injury is higher during nighttime crashes, TW-HMV (truck or bus) collisions, and hit-and-run crash cases where the colliding vehicle is unidentified. Older pillion riders are more vulnerable to sustaining fatal injuries in a crash. Crashes involving TWs hitting stationary objects and skidding are more fatal for pillion riders than other collision types.

Receivers and intermediary establishments typical to urban areas have unique freight behaviour and are understudied, particularly in developing countries. This paper uses establishment-based freight survey data to examine their freight generation patterns in the Chennai metropolitan area, India. Three sets of Simple Linear Regression (SLR) models, three single predictor proportional odds logit models, one multiple linear regression model, and one multiple predictor proportional odds logit model with business size (employment, area, and operational age) and indicators of establishment category as regressors are developed each for both freight production and freight attraction. Partial proportional odds logit models are developed in a few cases to overcome the limitations of proportional odds logit models. The best SLR model varied with the establishment category for freight production and attraction. The establishment area model is the best among single predictor proportional odds logit models. The multiple predictor proportional odds logit models marginally improved the fit over single predictor models. The proportional odds logit model results show that the establishment category has a greater impact on freight generation levels than business size variables. Since earlier studies rarely focused on receivers and intermediary establishments, policymakers may benefit from the developed models and study insights while estimating freight demand and developing freight policies.

Despite recent advancements in the Freight Trip Generation (FTG) modelling literature, there is a lack of understanding on the effect of the choice of a regression model, measurement period (daily/weekly FTG), and spatial dependence on model fit and freight-related policies. This study addresses these research gaps by developing non-spatial and spatial autoregressive multiple linear regression and count models for daily and weekly Freight Trip Production (FTP) and Freight Trip Attraction (FTA). We model Freight Shipments (FS) as FTP and Freight Deliveries (FD) as FTA. The results show that the best model for daily and weekly FTP is the spatial Zero-Inflated Negative Binomial (ZINB) model. The best daily and weekly FTA model is the non-spatial Negative Binomial (NB) model. The findings indicate the presence of spatial dependence in the best FTP model, while it is absent in the best FTA model. The elasticity analysis shows that daily models may lead to bias and inaccurate prediction of policy impacts. The study recommends using count models that capture more FTG characteristics with a week as the measurement period and consider spatial dependence, if present.

With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g. cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., Mobility as a Resource, MaaR). Further, due to increasing scarcity of shared mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility.

With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first- and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent- and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated.

Many rapidly developing countries around the world are at a crossroads when it comes to transportation, air quality, and sustainability. Indeed, the challenges presented by vehicular growth in India have motivated the search for sustainable transportation solutions. One solution constitutes ridehailing services, which are expected to reduce car ownership and provide affordable means of transportation. Another key solution is the rise of electric vehicles (EVs), which are expected to reduce greenhouse gas emission and address the growing demand for sustainable urban mobility. Using a unique survey data set collected in 2018 from a sample of 43,000 respondents spread across 20 cities in India, this paper attempts to shed light on the factors that affect adoption of on-demand transportation services and EVs in India. In particular, not only does this paper consider the socio-economic and demographic variables that affect these behavioral choices, but the modeling framework adopted in this study places a special emphasis on representing the important role played by attitudes, values, and perceptions in determining adoption of on-demand transportation services and EVs. It is observed that attitudes and values significantly affect the use of on-demand transportation services and EV ownership, suggesting that information campaigns and free trials/demonstrations would help advance the adoption of sustainable transportation modes. The model results help in the identification of policy options and infrastructure investments that can advance a sustainable transportation future in India.

Road crashes cause more than 1.5 lakh deaths in India every year. The crash severity enables us to understand the road crash and design mitigation measures to reduce road crashes’ severity. While most states in India do not maintain a comprehensive database of road crashes, all states have to file First Information Reports (FIRs) on road crashes reported to the police. FIRs are recorded in text format by a police person, and they contain descriptive information related to the severity of the road crashes. This FIRs text data can be used for road crash severity prediction. In this study, we have designed the road crash severity modeling as a text classification problem. We labeled 2969 FIRs of Tamil Nadu state to pre-defined crash severity classes: fatal, grievous, and minor. The study developed a bi-directional LSTM model, and it achieved an F-1 score of 90 percent in measuring road crash severity. The bi-directional LSTM model outperformed the random forest model and baseline model. The model developed in this study can be applied to FIRs data of other states of India for road crash severity prediction and can be used as a quick tool by policymakers and road safety researchers. This study is a step towards automatically developing a database of road crash severity for all road crashes occurring in the country since most states do not have a road crash database.

The drastic growth in the number of vehicles in the last few decades has necessitated significantly better traffic management and planning. To manage the traffic efficiently, traffic volume is an essential parameter. Most methods solve the vehicle counting problem under the assumption of state-of-the-art computation power. With the recent growth in cost-effective Internet of Things (IoT) devices and edge computing, several machine learning models are being tailored for such devices. Solving the traffic count problem on these devices will enable us to create a real-time dashboard of network-wide live traffic analytics. This paper proposes a Detect-Track-Count (DTC) framework to count vehicles efficiently on edge devices. The proposed solution aims at improving the performance of tiny vehicle detection models using an ensemble knowledge distillation technique. Experimental results on multiple datasets show that the custom knowledge distillation setup helps generalize a tiny object detector better.

During the last two decades, there has been substantial interest in developing freight trip generation (FTG) models. Most studies consider only truck trips or convert all freight trips into equivalent truck trips. Freight in several large cities is increasingly being moved by smaller vehicles. This calls for modeling FTG by vehicle type. The present research identifies and compares establishment characteristics affecting FTG by different vehicle types. In this context, spatial correlations among nearby establishments and the error-term correlations between independent models by vehicle type become relevant. Based on the Lagrange-Multiplier (LM) tests, we develop non-spatial seemingly unrelated regression (SUR) models for freight trip production (FTP) and spatial SUR models with a spatial lag in the dependent variable to account for both spatial and error-term correlations for freight trip attraction (FTA). The results show that establishment type and size affect FTG by different vehicle types.

With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first- and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent- and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated.

Emergency Medical Services (EMS) are a crucial part of the healthcare system. For evaluating the quality of EMS and better planning of EMS allocation to emergency requests, it is important to know the incident location for each emergency request. Currently in India, the callers requesting EMS verbally describe the incident location to which ambulances are dispatched. Therefore, incident locations are stored as descriptions and not as geo-coordinates. However, most EMS vehicles are equipped with GPS. There is a need for automated approaches to identify the incident locations from geospatial data of ambulances. This paper presents a system that uses a clustering approach followed by a distance heuristic implementation for effective identification of the incident locations from ambulance trajectories. Overall, for various levels of precision set for manual validation, the approach proves to be effective with an accuracy of 87.14%. The identified incident locations can be used for further analysis such as evaluating quality of EMS and planning and optimization of EMS allocation to emergency requests.

This study presents an integrated model to shed light on the factors influencing individuals' likelihood and frequency of usage of bus transit in Bengaluru, India, with a focus on the role of individuals' subjective peroceptions of service quality. Typically, subjective perceptions of transit service characteristics such as comfort, cleanliness, reliability, and safety are measured using Likert rating scale questions in travel surveys. A shortocoming with many such surveys is that the Likert rating scale questions do not include a "don't know" response category for the respondents to express their unfamiliarity and lack of opinion on the transit service. For this reason, some respondents who are not familiar with and do not have an opinion about the transit system are likely to choose the neutral response to Likert scale questions. At the same time, travelers who are familiar with and/or informed about the transit system may also choose the neutral response to state their opinion neutrality. As a result, some travelers' unfamiliarity with (and lack of opinion about) transit services may be confounded with the informed perceptions of those who are familiar with transit. This is because those who are unfamiliar with the transit system are less likely to use it and more likely to state neutral responses than those who are familiar with the system. Ignoring such influence of travelers' unfamiliarity can potentially distort the ordinal scale of Likert variables, result in biased parameter estimates and distorted implications about the influence of perceptions on transit usage. To address this concern, this study uses a generalized heterogeneous data model (GHDM) that allows a joint econometric analysis of the influence of individuals' perceptions of transit service quality on their likelihood of transit use and frequency of use and at the same time disentangle unfamiliarity from informed perceptions. The empirical results shed light on: (a) the role of individuals' demographic variables and subjective perceptions on their use and frequency of use of the bus transit system in Bengaluru, (b) the imporotance of separating unfamiliarity from informed opinions on transit service quality, (c) the need to include an option for respondents to reveal their unfamiliarity in Likert rating scale survey questions on perceptions, and (d) demographic segment-specific strategies for attracting new riders and enhancing ridership of current users of the bus transit system in Bengaluru.

Estimating emissions savings from alternative vehicular technologies is essential to streamline urban freight transport policy. We estimate freight emissions in the city of Chennai, India using real-world emission factors obtained from on-board emission measurement systems and trip characteristics collected through an establishment survey. India recently enforced Bharat Stage VI (BS-VI) emission standards and both the central and multiple state governments have introduced battery electric vehicle (BEV) policies. To understand potential benefits, we estimate the emission savings with BS-VI vehicles and BEVs for freight delivery. The BS-VI vehicles reduce tailpipe CO emissions by 20.3%, and HC+NOx emissions by 74.5%. The GHG emission saving in CO2 equivalents with BEVs is 50.3%, which increase with a greener energy mix, improved transmission and distribution efficiency, cleaner battery production, and more lifetime vehicle kilometres. BS-VI adoption can deliver significant emission reduction until BEVs and charging infrastructure scale up.

In this study, researchers have explored real-world driving conditions and developed emission factors for 58 passenger cars using on-board emission measurement technique while driving on five different routes in Delhi. The measured average emission factors of CO, HC, and NO were 3.99, 0.34, and 0.54 g/km for diesel vehicles, 7.26, 0.17, and 0.62 for petrol vehicles respectively. Road, traffic, vehicle type, and driving characteristics affect the quantity of emissions released. However, speed and acceleration significantly impact emission rates increasing with the increase in speed and acceleration. Also, emissions were minimal at 40-60 kmph and - 0.5-0.5 m/s2. The estimated city-wide CO, HC, and NO emissions were 60.8, 4.8, and 9.72tonnes/day. These results demonstrate the importance of monitoring the real-world exhaust emissions given the substantial difference between test cycle measurements used for compliance testing of new vehicles.

This paper proposes a continuum model based on a three-dimensional flow-concentration surface for multi-class traffic. The model assumes that the flow of any vehicle class is a function of the class density as well as the fraction of road area occupied by other vehicle classes. By considering occupancy of road area instead of lane occupancy, the model effectively describes traffic flow that does not follow lane discipline. The propagation speed of small disturbance (PSSD), conventionally defined from the two-dimensional flow–density relationship, is reformulated for each class using a three-dimensional flow–concentration surface. Using the proposed PSSD and a speed–area occupancy (speed- A O ) relationship, a second-order continuum model for multi-class traffic is formulated. The speed– A O relationship captures class-specific congestion and replicates the gap-filling behaviour commonly observed in lane-indisciplined traffic. Properties of the proposed model are validated theoretically where possible, and through numerical simulation when theoretical derivations are cumbersome. Numerical simulation of the proposed multi-class traffic model replicates field-observed phenomena such as shockwaves and rarefaction waves, local cluster effect, and gap-filling behaviour. Finally, the model is calibrated using field traffic data collected on a road section with bottleneck, and is found to replicate class-wise vehicle flows and speeds, and stop-and-go phenomena.

Ride-hailing services have grown in cities around the world. There are, however, few studies and even fewer publicly available data sources that provide a basis to understand and quantify changes in ride-hailing usage over time. Ride-hailing use may change over time because of socio-demographic shifts, economic and technological changes, and service attribute enhancements, as well as changes in unobserved attributes such as attitudes and perceptions, lifestyle preferences, technology savviness, and social influences. It is important to quantify the effects of these different forces on ride-hailing frequency so that robust forecasts of ride-hailing use can be developed. This paper uses repeated cross-sectional data collected in 2015 and 2017 in the Puget Sound region to analyze the differential effects of socio-demographic variables on the evolution of ride-hailing adoption and usage. By doing so, the study is able to isolate and quantify the pure effect of the passage of time on adoption of ride-hailing services. A joint binary probit-ordered probit model is estimated on the pooled dataset to explicitly account for sample-selection differences between the 2015 and 2017 surveys that may affect estimates of ride-hailing adoption in the two years. Model estimation results are used to compute average treatment effects of different variables on ride-hailing usage over time. It is found that the effects of most demographic variables on individuals' propensity to use ride-hailing are softening over time, leading to reduced differences in ride-hailing use among market segments. This suggests that there is a "democratization" of ride-hailing services over time.

Multi-class traffic flow modelling has various approaches several of which have focused on analytical proofs. A key limitation in this field of research is the limited field data applications. This study proposes a speed-gradient-based multi-class second-order model and shows its application to three different road sections, a mid-block section, a section with a bottleneck, and a section with a signal at the end, in Chennai, India. The model captures the congestion formation and dissipation phenomena well and could predict outflow and speed fluctuations generally observed in the field scenarios accurately. The prediction of traffic flow dynamics by the proposed model is also observed to be better when compared with two existing higher-order multi-class models.

Emissions from motor vehicles lead to significant adverse effect on air quality of cities, with many cities reporting ambient air concentration of pollutants well beyond the permissible standards. In this paper, we determine the effect of changing driving patterns during peak and off-peak periods on emissions from diesel passenger cars. Second-by-second emissions of CO, CO2, HC, and NOx were measured during both peak and off-peak periods using portable emission measurement system (PEMS). It is seen that during peak hour, average speed decreases, percentage time spent in acceleration, deceleration, and idling increases, while time spent in cruising mode decreases significantly. Further, EFs are developed for peak and off-peak periods and compared with the Automotive Research Association of India (ARAI) emission standards. The EFs during peak periods are found to be significantly different from off-peak periods. The results of this study would be useful in accurate quantification of emissions.

Although urban freight transport is a significant contributor to the development of a country, it has adverse effects on the environment and the quality of life in urban areas. To reduce these adverse effects, we can deploy sustainable city logistic strategies. Scheduling and routing of vehicles is a crucial decision in city logistic strategies. Hence, in this paper, we solve the Multi-Depot Two Echelon Capacitated Vehicle Routing Problem (MD2E-CVRP), which is a variant of Vehicle Routing Problem (VRP) with heterogeneous fleets at both levels. Since VRP is NP-hard, we have proposed a Simulated Annealing (SA) based heuristic solution algorithm and have tested it on the standard 2E- CVRP and MD2E-CVRP instances. The results obtained from SA have a good solution quality at just one-fiftieth of computational time using CPLEX and was found to be faster than Adaptive Large Neighborhood Search (ALNS) with only a marginal drop in solution quality.