Sabyasachee Mishra

Intelligent Transportation Systems-Enabled Optimal Emission Pricing Models for Reducing Carbon Footprints in a Bimodal Network

Scientists and policymakers intend worldwide emissions reduction of up to 80% of carbon dioxide (CO2) and other greenhouse gases (GHGs) in the next four decades to stabilize atmospheric concentrations. Henceforth, an immediate response from the transportation sector, one of the largest producers of GHGs (up to 30% in the United States), is critical for GHGs reduction. Recent advancement in intelligent transportation systems (ITS) offers a technical solution to implement emission pricing effectively in a reasonable period of time. Further, this strategy can foster demand for efficient vehicles and high transit ridership while reducing GHGs emission and generating revenue. Therefore, in this study, we propose models for understanding the reduction of GHGs emission and shifts of private vehicle trips to transit by implementing ITS-based optimal emission pricing to reduce GHGs emission by a certain percentage in a composite transportation network (transit and highway network). The bilevel models presented in this study take into account the planners policy decision and the road users response to such policies in a simple and methodologically robust framework. The complex decision of choosing transit over private vehicle and road user behavior in the study has been studied by mode split functions and the classical user equilibrium principle. The performance of proposed models is compared to the base case (do nothing); reductions in total GHGs emission by optimal emission pricing shows efficacy of the models. The presented methodology in this article is generalizable and can be applied to any transportation network.

A functional integrated land use-transportation model for analyzing transportation impacts in the Maryland-Washington, DC Region

Abstract The Maryland-Washington, DC region has been experiencing significant land-use changes and changes in local and regional travel patterns due to increasing growth and sprawl. The region’s highway and transit networks regularly experience severe congestion levels. Before proceeding with plans to build new transportation infrastructure to address this expanding demand for travel, a critical question is how future land use will affect the regional transportation system. This article investigates how an integrated land-use and transportation model can address this question. A base year and two horizon-year land use-transport scenarios are analyzed. The horizon-year scenarios are: (1) business as usual (BAU) and (2) high gasoline prices (HGP). The scenarios developed through the land-use model are derived from a three-stage top-down approach: (a) at the state level, (b) at the county level, and (c) at the statewide modeling zone (SMZ) level that reflects economic impacts on the region. The transportation model, the Maryland Statewide Transport Model (MSTM), is an integrated land use-transportation model, capable of reflecting development and travel patterns in the region. The model includes all of Maryland, Washington, DC, and Delaware, and portions of southern Pennsylvania, northern Virginia, New Jersey, and West Virginia. The neighboring states are included to reflect the entering, exiting, and through trips in the region. The MSTM is a four-step travel-demand model with input provided by the alternative land-use scenarios, designed to produce link-level assignment results for four daily time periods, nineteen trip purposes, and eleven modes of travel. This article presents preliminary results of the land use-transportation model. The long-distance passenger and commodity-travel models are at the development stage and are not included in the results. The analyses of the land use-transport scenarios reveal insights to the region’s travel patterns in terms of the congestion level and the shift of travel as per land-use changes. The model is a useful tool for analyzing future land-use and transportation impacts in the region.

Joint Travel Demand and Environmental Model to Incorporate Emission Pricing for Large Transportation Networks

Emission reduction strategies are gaining greater attention as a support of the national objective for a sustainable and green transportation system. A large percentage of the emission contribution that arises from transportation modes are primarily from auto and truck travel. Reductions in highway travel require prudent planning strategies and modeling user response to planners policies. Modeling planning goals and user response is a challenging task. In this paper a joint travel demand and environmental model incorporates vehicle emission pricing (VEP) as a strategy for emission reduction. First, the travel demand model determines the destination, mode, and route choice of the users in response to the VEP strategy set by the planner. Second, the emission model provides oxides of nitrogen, volatile organic compounds, and carbon dioxide estimates at a very detailed level. A base case and three models incorporate VEP in a multimodal transportation network. The objective function of the base case is the minimization of total system travel time, and the models are designed with the objective of minimizing total system emissions. The user equilibrium method is used for travel to model user responses and is solved by the Frank–Wolfe algorithm. The base case represents do-nothing conditions, and the three models address the interactions between planners perspectives and user responses to VEP strategies. The proposed model is applied to the multimodal transportation network of Montgomery County (located in the Washington, D.C.–Baltimore, Maryland, region). The case study results show that VEP can be used as a tool for emission reduction in transportation planning and policy.

Analysis of injury severity of large truck crashes in work zones

Work zones are critical parts of the transportation infrastructure renewal process consisting of rehabilitation of roadways, maintenance, and utility work. Given the specific nature of a work zone (complex arrangements of traffic control devices and signs, narrow lanes, duration) a number of crashes occur with varying severities involving different vehicle sizes. In this paper we attempt to investigate the causal factors contributing to injury severity of large truck crashes in work zones. Considering the discrete nature of injury severity categories, a number of comparable econometric models were developed including multinomial logit (MNL), nested logit (NL), ordered logit (ORL), and generalized ordered logit (GORL) models. The MNL and NL models belong to the class of unordered discrete choice models and do not recognize the intrinsic ordinal nature of the injury severity data. The ORL and GORL models, on the other hand, belong to the ordered response framework that was specifically developed for handling ordinal dependent variables. Past literature did not find conclusive evidence in support of either framework. This study compared these alternate modeling frameworks for analyzing injury severity of crashes involving large trucks in work zones. The model estimation was undertaken by compiling a database of crashes that (1) involved large trucks and (2) occurred in work zones in the past 10 years in Minnesota. Empirical findings indicate that the GORL model provided superior data fit as compared to all the other models. Also, elasticity analysis was undertaken to quantify the magnitude of impact of different factors on work zone safety and the results of this analysis suggest the factors that increase the risk propensity of sustaining severe crashes in a work zone include crashes in the daytime, no control of access, higher speed limits, and crashes occurring on rural principal arterials.

Evaluation of the floaterm concept at marine container terminals via simulation

Abstract International seaborne trade rose significantly during the past decades. This created the need to increase capacity of existing marine container terminals to meet the growing demand. The major objective of this paper is to evaluate the floaterm concept using simulation modeling and determine if it can improve terminal productivity. The main difference between floaterm and conventional marine container terminals is that, in the former case, transshipment containers are handled by off-shore quay cranes and stored on container barges. Two terminal configurations performance is compared (vessel handling times and equipment utilization) under normal and disruptive conditions. Computational experiments confirm preliminary expectations that the floaterm concept can enhance efficiency of marine container terminal operations under normal and disruptive conditions.

Diverging Diamond Interchange Analysis: Planning Tool

Many traffic simulation software tools have emerged over the last decade and are widely used for analyzing capacity, delay, and level of service at intersections, ramps, and along arterial/freeway segments. Although these tools have shown great promise, they are expensive and the data collection and input setup is time consuming and resource intensive. Traffic engineers predominantly use one of those tools to analyze a diverging diamond interchange (DDI), also known as double crossover diamond interchange. Developing a simulation model and performing required analysis takes considerable time. Because it is not necessary to obtain a detailed traffic operational analysis of a DDI while interchange alternatives are being developed, a quick and easy evaluation procedure is warranted. In this paper, a critical lane volume (CLV)-based DDI analysis methodology is developed, which could be an appropriate tool to bridge the gap. In this methodology, two intersections or nodes of a DDI, where through-traffic movements along the arterial cross each other, are considered crucial. Understanding of the crossover movements, ramp movements, and coordination of traffic movements between the two nodes and lane configuration are used in developing the methodology. Critical movements are analyzed, compared, and logically added to obtain the CLV of the two nodes. The obtained CLV is used in deriving the level of service of the two intersections in a DDI. The paper describes the mathematical formulation and analysis procedure to evaluate a DDI. Two real-world DDIs are analyzed by using the developed method and compared with simulation results for reliability and accuracy.

Prediction of secondary crash frequency on highway networks

Secondary crash (SC) occurrences are major contributors to traffic delay and reduced safety, particularly in urban areas. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, determining the primary contributing factors for their occurrence is crucial. The non-recurring nature of SCs makes it imperative to predict their occurrences for effective incident management. In this context, the objective of this study is to develop prediction models to better understand causal factors inducing SCs. Given the count nature of secondary crash frequency data, the authors used count modeling methods including the standard Poisson and Negative Binomial (NB) models and their generalized variants to analyze secondary crash occurrences. Specifically, Generalized Ordered Response Probit (GORP) framework that subsumes standard count models as special cases and provides additional flexibility thus improving predictive accuracy were used in this study. The models developed account for possible effects of geometric design features, traffic composition and exposure, land use and other segment related attributes on frequency of SCs on freeways. The models were estimated using data from Shelby County, TN and results show that annual average daily traffic (AADT), traffic composition, land use, number of lanes, right side shoulder width, posted speed limits and ramp indicator are among key variables that effect SC occurrences. Also, the elasticity effects of these different factors were also computed to quantify their magnitude of impact.

Incorporating uncertainty and risk in transportation investment decision-making

This paper presents a framework for addressing uncertainty and risk for large-scale transportation investments involving public–private participation. Demand, fare/toll and demand responsive costs are considered in the uncertainty analysis. Uncertainty analysis provides information on economic feasibility of the project. A set of relaxation policies is proposed to form various Ownership, Tenure and Governance (OTG) strategies reflecting the nature and level of participation by the public and private entity. A Monte Carlo Simulation-based Value at Risk is used to quantify risk. Finally, a methodology is proposed to integrate uncertainty and risk. The framework is tested on the proposed multibillion dollar Detroit River International Crossing connecting the cities of Detroit in the USA with Windsor in Canada. The analysis provides insights to probable outcomes for this transportation infrastructure investment under different OTG scenarios.

A tool for measuring and visualizing connectivity of transit stop, route and transfer center in a multimodal transportation network

Agencies at the federal, state and local level are aiming to enhance the public transportation system (PTS) as one alternative to alleviate congestion and to cater to the needs of captive riders. To effectively act as a viable alternative transportation mode, the system must be highly efficient. One way to measure efficiency of the PTS is connectivity. In a multimodal transportation system, transit is a key component. Transit connectivity is relatively complex to calculate, as one has to consider fares, schedule, capacity, frequency and other features of the system at large. Thus, assessing transit connectivity requires a systematic approach using many diverse parameters involved in real-world service provision. In this paper, we use a graph theoretic approach to evaluate transit connectivity at various levels of service and for various components of transit, such as nodes, lines, and transfer centers in a multimodal transportation system. Further, we provide a platform for computing connectivity over large-scale applications, using visualization to communicate results in the context of their geography and to facilitate public transit decision-making. The proposed framework is then applied to a comprehensive transit network in the Washington-Baltimore region. Underpinning the visualization, we introduce a novel spatial data architecture and Web-based interface designed with free and open source libraries and crowd-sourced contextual data, accessible on various platforms such as mobile phones, tablets and personal computers. The proposed methodology is a useful tool for both riders and decision-makers in assessing transit connectivity in a multimodal transit network in a number of ways such as the identification of under-served transit areas, prioritization and allocation of funds to locations for improving transit service.

Cross-Nested Joint Model of Travel Mode and Departure Time Choice for Urban Commuting Trips: Case Study in Maryland Washington, DC Region

The aim of this paper is to contribute to describing the simultaneous choice of travel mode and departure time by making use of a cross-nested logit structure that allows for the joint representation of interalternative correlation along the both choice dimensions. Traditional multinomial logit model and nested logit model are formulated respectively. The analysis uses the revealed preference data collected from Maryland-Washington, DC, regional household travel survey during 2007–2008 for commuting trips, considering more work-related characteristics than previous studies. A comparison of the different model results shows that the presented cross-nested logit structure offers significant improvements over multinomial logit and nested logit models. The empirical results of the analysis reveal significant influences on commuter joint choice behavior of travel mode and departure time. Moreover, a Monte Carlo simulation for two groups of scenarios arising from transportation policies, congestion pricing, and improvements to transit service during peak period is undertaken respectively to examine the impact of a change in car travel cost and transit travel time on the travel mode and departure time switching. The simulation results show that US