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Trip-based models and activity-based models represent two extreme ends of the spectrum of travel demand models in data granularity requirement and ability to reflect the underlying motivation to travel. Modelling of representative freight activity-travel patterns (RFAPs) has the potential to serve as the bridge between these approaches. RFAP clusters represent homogeneous groups of establishments, where utility maximization models predict the probability that an establishment belongs to a particular cluster. However, it is still an open question how to define, interpret and model activity-travel patterns in the context of freight system. To answer this question, this study conducted a large-scale establishment-based freight survey (EBFS) in seven cities of India and resulted in a sample of 432 establishments and their 1613 shipment records. In the first part, this paper proposes a novel approach for identifying RFAPs based on the notion that "activities" that inspire trip-making for passenger is equivalent to "freight orders" in the case of establishments. The cluster analyses revealed the presence of three well separated main clusters and nine less separated nested clusters. Through interpretation and labelling of these RFAPs, freight travel market is categorized into useful segments. The results suggested that a priori industrial classification systems used in trip-based models are overly simplified representations of the complex structure of the travel patterns. In the last part, freight activity-travel pattern generation (FAPG) models are developed which predicts the probability that an establishment exhibits a particular RFAP. The FAPG models developed using these RFAPs could replace the traditional freight generation (FG) and freight trip generation (FTG) models due to its ability to convert the assigned activity-patterns to trips or tonnage. For example, FAPG model suggest that at an employment level of 120, there is a 56% probability that establishments will exhibit MDV-HFMH (medium duty vehicles - high frequency medium haul) pattern which, in turn, implies that FP = 1630 tons/year; shipment frequency, i.e., FTP = 8 trips/week; length of haul = 240.6 km and commercial vehicle type choice = MDV. Thus, FAPG models can present an enhanced representation of freight flows since both FG and FTG are jointly modeled in this approach. That is, the best features of both commodity-based modelling (i.e., ability to capture the fundamental mechanism that drives freight demand) and vehicle-based modelling (i.e., ability to capture freight traffic implications) are included in FAPG models. The study findings are expected to assist in identifying the variations in establishments' preferences so that it is possible to identify the type of transport supply improvements that the establishments will respond to accurately, and thus prioritize the infrastructure investments. Moreover, the discussions on these findings are expected to improve the behavioral and spatial foundations of traditional freight models. • Defines "freight orders" as logical equivalent of "household activities" that drive trip-making. • Activity based approach is used for modelling freight travel using EBFS data. • Identifies homogeneous segments of representative freight activity-travel patterns (RFAPs). • Propounds freight activity-travel pattern generation (FAPG) models based on RFAPs. • FAPG gives enhanced representations of freight flows (FG, FTG, vehicle type, haulage). [ABSTRACT FROM AUTHOR]

Non-response is unavoidable in any disaggregate-level survey efforts where information is sought from individuals. There is a strong need to understand the contributing factors of non-response behavior in freight surveys as there is very little information on what sort of establishments respond positively to survey inquiries. To meet this research gap, this paper analyzes the non-response behavior in an establishment-based freight survey (EBFS) conducted in Kerala, India. The analysis involves usage of five prominent classifiers: classification trees, random forests, Naïve Bayes classifier, logistic regression and K nearest neighbors. A closer look at the results revealed that the industrial classes handling commodities with high value density (machinery, electronics and electrical equipment, other manufacturing products, textiles) exhibit high non-response probability for freight surveys. This may be explained in terms of the limited logistic information shared by establishments handling high valued products due to less attention given to logistics strategies. Another reason could be that the amount of capital and opportunity costs tied up with these commodities are high, and therefore, their information tend to be more proprietary in nature. The analysis also reveals that the location of establishment plays a modest, yet, noticeable effect on non-response behavior. The establishments from cities located closer to Ports with high per-capita income are less likely to respond to survey requests, possibly due to the higher opportunity cost for time and increased concerns of intrusion into privacy. Finally, the comparison of ROC curves suggests that KNN algorithm is most suitable for modelling non-response behavior. These models are expected to be useful for developing sample weighting schemes and targeted incentive strategies that can improve the state of practice of freight data collection in middle-income countries like India. The study findings are expected to be useful for developing more elaborate and dynamically responsive survey designs for EBFS, in which sample recruitment strategies can be adapted real time during data collection. This study also informs policymakers that the apparent trends in non-response, if not arrested, are likely to weaken the validity of inferences drawn from estimates based on EBFS and undermine, perhaps fatally, the potential of EBFS to guide facility planning and policy interventions. • Study findings demonstrate a critical sampling problem that is often overlooked in freight surveys. • First research to investigate the underlying patterns in EBFS non-response. • Establishments handling high valued commodities are likely to be underrepresented in EBFS samples. • Establishments in cities closer to Ports with high per-capita income show reduced response propensity. • ROC curves suggest that KNN algorithm is most suitable for modelling non-response behavior. [ABSTRACT FROM AUTHOR]

The relocation of logistics facilities toward the periphery of urban areas—that is, logistics sprawl—is a major challenge for policymakers due to the impacts on the economic geography of suburban areas and associated changes in truck traffic pattern. The logistics sprawl considerably affects the vehicle kilometers traveled (VKT) since logistics facilities are the origin or destination nodes for urban freight transport. The effect of logistics sprawl, coupled with industry category, employment size, fleet ownership, and cohort on the VKT, is an unaddressed research area that requires significant research attention. To quantify the effects of logistics sprawl on the VKT, this paper utilizes establishment-based freight survey (EBFS) data to perform a cross-sectional analysis of urban and suburban logistics facilities. The distinct variations in the effects of logistics sprawl across multiple industry types are investigated using the EBFS data. The combined model indicated that the VKT generally increases as an establishment is located away from a city center. Compared with the establishments' VKT in Sprawl 1, the VKT for establishments in Sprawl 2, 3, and 4 increases by 6.39, 4.12, and 6.06 km, respectively. Classified industry model results demonstrated that the sprawl effect on the VKT is industry specific. Establishments dealing with food products demonstrated a positive relation between the average VKT and the distance of an establishment from a city center. However, the sprawl effect was not consistent for establishments dealing with wood products, plastic and rubber products, mineral products, and metal and metal-related products. Moreover, the developed cohort model indicated an average VKT increase by 29.42 and 46.43 km for establishments that started operations in the postindustrialization and postliberalization periods, respectively, compared with the establishments being operational from the postindependence period. This investigation's tangible outcome is to present the influencing factors of the VKT, which will be beneficial for policymakers to develop industry-specific guidelines on land use, employment restructuring, and subsidies on owning different types of commercial vehicles. [ABSTRACT FROM AUTHOR]

Saturation flow rate (SFR) variations were analyzed using the video data collected at a signalized intersection in Winnipeg, Canada, to investigate the implications of adverse road-weather (RW) conditions for SFR distributions and characteristics. Survival analysis was implemented to develop stochastic SFR distribution functions considering censored data and a statistical analysis method was developed for determining the optimal critical vehicle (CV) for measurement of saturation headway. The analysis findings suggest that adverse RW conditions decrease SFR significantly and moves CV to the front of the queue while having little impact on SFR considering heavy vehicles. Furthermore, the findings imply that the conventional SFR estimation method overestimates the probability of saturation at a given flow rate. The proposed analysis method reveals stochastic characteristics of SFR and provides a method to estimate SFR distributions under different RW conditions, which is essential for improving the operation of signalized intersections, particularly in cold regions. [ABSTRACT FROM AUTHOR]

The relationships between travel attitudes and mode choices are widely used to explain travel satisfaction as an outcome of individual decisions. However, studies analyzing gender disparities in travel satisfaction levels are still missing in literature, resulting in a dearth of knowledge about gendered aspects of travel satisfaction. This study contributes to this discernible research need by analyzing the complex interplay between attitudes, behavior, and travel satisfaction. The extent of travel mode consonance (i.e., individuals travelling in their preferred modes) and dissonance (i.e., individuals travelling in a non-preferred mode) in various multimodal travel segments are analyzed first for quantifying the links between attitudes and behavior. Subsequently, gender disparities in travel satisfaction levels are assessed using gap hypothesis tests. The findings from this study analysing 897 responses from Delhi, India, provide a developing-country perspective to the emerging international research on travel satisfaction and create an evidence base for decreasing gender disparities in the urban mobility. Our results indicated that multimodal choices are strongly linked to mode specific attitudes and most of the consonant travellers fall under car users and most of the dissonant travellers come under public transport. [ABSTRACT FROM AUTHOR]

Most of the past research on public attitudes or choices on electric vehicles (EVs) have used user survey data (i.e., at a microscopic level). This paper adopts a multi-method approach to conduct a data-driven exploration of public sentiment evolution using a public tweet database spread across the last decade (2012–2022) (i.e., at a macroscopic level). Data mining from Twitter has enabled this research to obtain a rich alternative source of public sentiments on EVs. Natural Language Processing (NLP) techniques were then used to analyse the evolution in sentiments and reveal the underlying patterns in the discourse on EVs. Results and discussions in this paper categorise the shifts in public opinion and recognise critical linkages of economic summits or global events to sentiment changes. The research outcomes are expected to offer a strategic vantage point on the evolution of public discourse related to EV adoption and assist in designing better transportation electrification policies. [ABSTRACT FROM AUTHOR]

Abstract The state-of-practice in planning, designing, and conducting freight surveys leave much to be desired, even in the era of big data analytics. This paper addresses this issue by providing a comprehensive, yet, inexpensive integrated data collection framework for conducting establishment-based freight survey (EBFS). The paper demonstrates the application of the proposed framework by implementing it in eight cities across two geographically dissimilar states of India. This is the first freight survey of its kind in terms of scale and scope in developing countries, where there is no established practice in freight data collection. Guidelines are suggested for overcoming challenges in EBFS such as: (a) efficient allocation of survey resources within budgetary constraints; (b) effective survey instrument design for reducing the respondent burden; (c) determination of sample size requirements and the expected number of sampling units to be contacted (d) development of sampling strategies using sampling frames with limited auxiliary information; and (e) data collection strategies to improve response rates. The heuristics for allocation of survey resources are mathematically formulated and predicted using the web-based survey responses obtained from planners of past surveys. Trade-off scenarios between different components of survey resources (money, time, and manpower) are presented to enable the planners to arrive at a suitable EBFS design for meeting research requirements within resource constraints. Analysis of results suggest that the response rates for EBFS are largely linked to the physical characteristics of commodities and city demography. The discussions provided on resource allocation, survey instrument design, sampling design, pilot surveys, interviewer training, response rate improvement strategies, and data processing are expected to guide for best practices in future surveys. In sum, the literature synthesis, case-study illustrations, and the proposed framework for EBFS design are expected to strengthen the state-of-practice of EBFS by making the rigorous random sample surveys less expensive, more systematic, and in turn, replacing the need to opt for convenience samples. Highlights • A structured synthesis of establishment freight surveys (EBFS) is presented and insights are drawn. • An integrated comprehensive framework is proposed for planning, designing, and conducting EBFS. • Guidelines and tools provided for resource allocation, survey instrument design, sampling and data collection strategies. • Survey design framework is implemented, and findings are reported for two dissimilar states in India. • Survey response rates found to be influenced by physical characteristics of commodities and city demography. [ABSTRACT FROM AUTHOR]

This paper presents a set of freight generation (FG) models for seven cities in Kerala, India. Models were formulated by considering business size variables that capture establishment freight activity. Model estimation results revealed that business size indicators such as number of employees and gross floor area explained the freight generation well. Number of years in business ( YB ) was identified as another variable and considered in model specification to estimate FG. Detailed investigation of single variable FG models suggested that employment-based models are suitable for cities with dense commercial activities and higher land value, while area-based models better represented FG in cities with medium level of urbanization. Area appears to be a skewed indicator for representing business size in cities with dense commercial activities. In this case, where acquiring area is difficult, employment may be a better representative of growth in freight activity. As an extension of city specific FG models, three types of combined FG models were developed to provide quantitative statistical evidence for differences in model specifications across the cities. The statistical findings from these models suggest that freight activities are influenced by the interaction of establishment characteristics and its location. Interaction effect is more prominent when area is used to represent the business size. Since a systematic commodity flow survey practice is absent in India, planners and policy makers can be benefited from this study while making decisions on freight specific investment schemes and freight operation strategies. The interaction FG models discussed subsequently in this study may be utilized in transportation planning application for the state, regional and corridor level network capacity needs. [ABSTRACT FROM AUTHOR]

Rise in length of haul (LOH) of business establishments and the associated greenhouse gas (GHG) emissions are a major concern for planners and policymakers, require extensive investigation. This paper quantifies various factors like business age, gross floor area, employment, the build environment variable on LOH, and associated GHG emission using EBFS data. Survival analysis has been utilized for the analysis. The aggregated LOH model provides a comparative analysis of LOH across different industry sectors and demonstrate that gross floor area, business age, and employment are positively associated with LOH. Subsequently, disaggregated LOH models are reveal the influencing parameter of LOH of a particular industry category. Lastly, GHG models show the influence of fleet ownership level on GHG emissions across different industry sectors. The outcomes will be beneficial for planners and policymakers to develop industry-specific regulations on land use, employment restructuring, and subsidies for owning different types of commercial vehicles. • Effect of various establishment characteristics on length of haul (LOH), and associated GHG emission have been quantified. • Survival analysis has been utilized to determine the influencing factors of LOH and associated GHG emission. • Three different models: Aggregated industry sector model, Disaggregated industry sector model and GHG emission model have been developed. • The understanding of this research on LOH and associated emissions provides clear insights for urban planners about the factors resulting in enhanced LOH and emission. [ABSTRACT FROM AUTHOR]

• Freight trips are modeled as a joint package consisting of outsourcing and insourcing decisions. • Multivariate modelling framework is used to account for error correlations between freight trips. • Substitutive relationship found between insourced and outsourced trips using same truck type. • Models reveal the exogenous effects of busines size, age, fleet ownership and industry sector. • Study findings highlight the drawbacks in the current approach of modelling overall freight trips. Despite the advancements in the logistics choices, freight trip generation (FTG) studies in the past have made no distinctions between insourced and outsourced variants of trips and the associated selection of truck types. This paper addresses past research gap by treating FTG as a joint package decision and proposing a multivariate ordered response system that allows error correlations capturing the underlying interdependencies. Study findings reveal the presence of complementary and substitution effects between freight trip decisions, and the additional computation burden resulting from the simultaneous model estimation appears to be justified for improving the logistical foundations of FTG models. [ABSTRACT FROM AUTHOR]

Commercial Vehicle (CV) ownership is a variable of interest in planning interventions and policy instruments for sustainable transportation, but the decision-making process to own a vehicle fleet is not well understood in the context of freight transportation. The necessity to understand the heterogeneity in CV type ownership arises from the need to mitigate externalities associated with them. We argue that the need to own CVs is a derived demand arising out of the demand to move goods and is influenced by freight handling requirements of businesses. To this end, this paper examines the dual role (mediation) of freight generation variables while modelling CV ownership, which is an underexplored area of research in freight transportation planning. Establishment-based freight survey data from seven cities in Kerala, India, are used to develop structural equation models. The CV ownership is analysed through freight generation variables as mediators, apart from the direct influence of business size indicators and commodity characteristics. The study findings confirm the intermediatory role of freight generation and show the consequences of neglecting such a dual role in the overall CV ownership modelling paradigm. The model estimates offer insights into the causal structure of the decision-making process of establishments to own different levels of fleet ownership and composition. Understanding the business managers' decisions to own CVs in their fleet can be used for freight traffic management using urban consolidation centre (UCC). The statistical findings also provide the total effects between fleet and employment size and how freight generation mediates this relationship. The path diagrams representing the CV ownership patterns can be useful to policy makers while implementing policies like low-emission zones. • Heterogeneity in commercial vehicle ownership decisions are examined. • Dual role (mediation) of freight generation in CV ownership is revealed in path diagrams. • Causal structure of fleet ownership and composition decision-making process is revealed in models. • Employment size is having greater positive effects on ownership of medium and heavy-duty vehicles. [ABSTRACT FROM AUTHOR]

This paper presents the results of the first driver behavior questionnaire (DBQ) study on analyzing Indian drivers’ aberrant behavior for red light violations (RLVs) at signalized junctions. An ordered probit model is developed using the drivers’ socio-demographic variables. The model results indicate that age has the strongest influence on drivers’ RLV behavior, followed by driving experience and level of education. The level of perceived RLV appears to increase with higher driving frequency and two-wheeler ownership. This research is also the first to analyze the aberrant behavior differences among male and female drivers concerning their demographics and driving experiences. It is unique to note that at a certain combination of various demographics (e.g., driving experience, education level, and daily trip frequency), the men and women are becoming equal in their aberrant behavior while committing RLVs. This finding is an interesting input for developing driver training and education programs. [ABSTRACT FROM AUTHOR]

• Establishment-level analysis to put a price on greenhouse gas emission in freight transport sector. • Latent class analysis to divide establishments based on fleet-ownership patterns. • Examines the determinants of freight transport emission disaggregated by fleet segments. • Gross emitters in each fleet ownership segment are modeled for targeted policy interventions. • Implications for designing carbon tax and trading programs in developing economies like India. Concerns over transport energy consumption and greenhouse gas emissions have lately prompted significant attention towards vehicle ownership patterns and the distributional implications of climate mitigation policies. This paper contributes to freight transport literature by examining how establishments can be meaningfully grouped using their fleet ownership pattern (size and composition) and how these segments vary in their freight travel pattern and CO 2 emission levels. The analysis performed using an establishment-based freight survey dataset from Kerala, a coastal state in India, revealed five latent classes of fleet ownership. The determinants of freight transport emissions and the gross emitters in fleet segments are presented subsequently by analyzing how the establishment-level characteristics (employment, business age, industry sector, value density, and relative location) explain the variation in CO 2 emissions. The study findings offer insights for designing effective carbon policy instruments and incentive schemes to regulate the establishment-level freight travel pattern and control transport emissions. [ABSTRACT FROM AUTHOR]

• Acceptance of delivery robots analyzed in the context of global spotlight on contactless deliveries. • Contingent valuation method used to evaluate consumers' willingness to pay for delivery robots. • Issue of consumer heterogeneity in acceptance is addressed using a latent class model. • Attitude-based segmentation revealed six underlying consumer segments. • Demographical and psychological determinants of willingness to pay are discussed. Autonomous delivery robot (ADR) technology for last-mile freight deliveries is a valuable step towards low-carbon logistics. The ongoing COVID-19 pandemic has put a global spotlight on ADRs for contactless package deliveries, and tremendous market interest has been pushing ADR developers to provide large-scale operation in several US cities. The deployment and penetration of ADR technology in this emerging marketplace calls for collection and analysis of consumer preference data on ADRs. This study addresses the need for research on public acceptance of ADRs and offers a detailed analysis of consumer preferences, trust, attitudes, and willingness to pay (WTP) using a representative sample of 483 consumers in Portland. The results reveal six underlying consumer segments: Direct Shoppers, E-Shopping Lovers, COVID Converts, Omnichannel Consumers, E-Shopping Skeptics, and Indifferent Consumers. By identifying the WTP determinants of these latent classes, this study provides actionable guidance for fostering mass adoption of low-carbon deliveries in the last-mile. [ABSTRACT FROM AUTHOR]

There is a growing interest in incorporating spatial indicators into freight demand model systems. The indicators are measured for different areal units (e.g., census tracts or block groups) and are often used as proxy variables or aggregation layers. Model estimation results vary according to the choice of these areal units and an analyst is thus confronted with a popular decision challenge termed as 'modifiable areal unit problem' (MAUP). The variability in results due to MAUP arises since areal units can be modified in theoretically infinite ways (in terms of shape, orientation and size) and magnitude of aggregation loss in information will vary for each alternative zoning system. In effect, how well the zonal (aggregated) characteristics can describe the establishment-level (disaggregated) observations is inversely related to MAUP effects. Little is known, however, about the extent of MAUP effects in freight generation (FG) models and freight trip generation (FTG) models. This study diagnoses the implications of MAUP effects in FG and FTG models by designing alternate zoning systems (by means of different zonal variables and clustering techniques) and assessing the sensitivity of model estimation results within a framework of comparative analysis (by means of hierarchical linear models). Study results assess the presence of MAUP as alternate zoning systems resulted in wide variation in the estimated coefficients for zonal characteristics (e.g., industrial area, land value, number of establishments, distance to primary arterial) in terms of magnitude, statistical significance, and even in the direction of association (sign of the coefficient). The implication of this finding is that an analyst may design different or even counterproductive policy instruments based on the way data is aggregated to capture the role of land-use, spatial effects and built-environment in influencing freight travel patterns. MAUP effects are also found to be dependent on the metric in which freight is measured (i.e., FG or FTG) and direction in which flow is measured (i.e., production or attraction). Overall, this research improves the understanding of the parameter sensitivity and performance sensitivity of freight demand model systems to alternative spatial representations of an establishment's relative location. The research findings strongly encourage analysts to acknowledge that the results of freight travel analyses with spatial indicators are sensitive to the definition of areal units. • A novel attempt to examine the potential impacts of MAUP in measurement of FG/FTG patterns • A methodology is proposed to compare different zoning systems for developing FG/FTG models • Effects of zonal characteristics on disaggregated freight travel (FG/FTG patterns) are explored • Research findings offer insights for spatial data aggregation in freight demand model systems • Results reveal that freight travel analyses with spatial indicators are sensitive to the definition of areal units [ABSTRACT FROM AUTHOR]

This paper contributes to the emerging literature on freight studies by identifying the optimal freight traffic analysis zone (FTAZ) system at which to conduct macro-level freight travel analysis. To arrive at the optimal scale, we develop alternate zone systems by grouping census wards with similar freight-related characteristics (industrial characteristics, commercial land use characteristics, locational characteristics and socio-demographic characteristics). The resultant zone systems are analysed at multiple geographic scales and the optimal scale of each zone system is determined by performing the Brown–Forsythe test. Results suggest that a 1:3 aggregation ratio (24–28 zones) is the optimal scale for Metropolitan FTAZs, whereas the publicly available ad-hoc zone system and prior literature on National FTAZs follow 1:10 aggregation. The study findings suggest that Metropolitan planning organizations need to reconsider their existing data collection strategy, consider a larger aggregation ratio and, by extension, adopt smaller zones to ensure that both local and global freight travel characteristics are captured in freight travel analyses. [ABSTRACT FROM AUTHOR]

The study is intended to investigate the effect of traffic composition and the use of a paved emergency lane on capacity. Ahmedabad-Vadodara four-lane divided intercity expressway (Mahatma Gandhi expressway, designated as NE-1) which has an emergency lane on either side is taken as a case study. For modeling traffic flow on expressway, a microscopic simulation technique is employed in this study. The simulation model was developed, calibrated, and validated for rendering as well as replicating the real traffic conditions. Results of the study show that the capacity is greatly influenced by emergency lanes and traffic composition. The result of the present study is expected to highlight the ongoing national-level efforts of developing guidelines for Indo-Highway Capacity Manual. [ABSTRACT FROM AUTHOR]

[Display omitted] • Effect of WMAs on moisture resistance of asphalt mixture is evaluated. • Chemical based additive showed better moisture resistance. • Adhesive and cohesive failure modes were assessed. • BSR and TSR showed appreciable correlation. • A limiting value of 0.7 is proposed for BSR. Moisture damage has been identified as a major concern for asphalt pavements. This concern becomes more critical when the asphalt mixtures are prepared at lower mixing and compaction temperatures using technologies such as warm mix asphalt (WMA). In this study, the effect of different WMA technologies on the moisture resistance of asphalt mixtures was studied using a variety of experimental investigations including bond strength ratio (BSR), percentage coating, retained Marshall stability (RMS), and tensile strength ratio (TSR). Two aggregate sources (granite and dolomite), two base asphalt binders (VG30 and PMB40), and five WMA additives categorized under organic, chemical, and foaming technologies were used. Moisture damage in WMA mixtures was found to be a function of aggregate type and base asphalt binder. Rediset, a chemical WMA additive, showed higher moisture resistance in comparison to other technologies. Appreciable correlations were found between BSR and TSR (R2 ranged from 0.61 to 0.72). This study suggests a minimum BSR of 0.7 for satisfactory performance against moisture damage. [ABSTRACT FROM AUTHOR]

• First study to capture shopping behavior of food desert and food oasis residents. • Explores shopping behavior in terms of both in-person trips and internet orders. • Compares the acceptance of autonomous delivery to replace physical shopping. • Residents planning to use ADRs for their internet order prefer them for all physical shopping trips. • Offers actionable guidance to overcome the mobility inequalities in food deserts. Food desert communities in the US have a widely recognized gap between the demand for healthy foods and the minimum order size that makes it worthwhile for food purveyors to deliver to such neighborhoods, thereby creating delivery deficiencies. A diverse set of mobility constraints and activity-travel patterns exist for disadvantaged segments in these communities, especially the elderly, unemployed, and socially excluded. Appreciating this complexity, an effective solution would be to improve the food access of such communities by providing faster, inexpensive, and flexible online deliveries of healthy foods. However, little is currently known about the shopping travel pattern in food desert communities and the associated mobility inequalities. This paper fulfills this critical research gap and quantifies the differences in shopping travel behavior observed among consumers residing in food deserts and food oases using data collected from Portland and Nashville Metropolitan areas. The paper subsequently captures the perceived acceptance of autonomous delivery robots (ADRs) among these consumers to overcome their mobility inequalities. The results indicate that food desert residents aged between 18 and 25 years, African Americans and those earning more than $75,000 are more likely to engage in internet shopping than food oasis residents. Despite the perceived potential of ADRs to reduce the mobility inequalities in food deserts, acceptance levels for this emerging technology are found to be significantly less among food desert residents, especially among older generational cohorts and less qualified. This study will provide key takeaways to e-commerce companies to expand their delivery service through ADRs in underserved areas. [ABSTRACT FROM AUTHOR]

• This study has identified and segmented key risk factors associated with fatal road crashes in Hyderabad, India. • Based on Police records, a comprehensive road crash database is developed for Hyderabad. • Cross-sectional study is used to test the association between the risk factors and fatal crashes using Risk-Ratio(RR) • Apriori algorithm was applied to identify association rules involving two or more risk factors leading to fatal crashes. • Risk factors were classified into six specific segments: Very high, High, Moderate, Low, Very low, and Extremely Low-risk factors. • Pedestrian as a victim, Heavy Motor Vehicles (HMV) as accused, were identified as very high-risk factors leading to fatal crash outcomes. This study presents a comprehensive methodological approach for the identification and segmentation of the key risk factors associated with fatal road crashes. Hyderabad, an Indian metropolis with significant annual fatal crashes, is selected as the case study city. Data containing the date, time, and location of the crash, number of injuries and fatalities, accused and victim vehicle details are collected from Hyderabad Traffic Police, and a comprehensive registry-based crash database is developed. Based on the database, a Cross-sectional study is conducted, and risk ratio (RR) is used as a measure to test the association between the risk factors and fatal outcomes. Logistic regression, log-binomial regression, and robust Poisson regression models were also used to understand the association of fatal crash outcomes with different attributes. RR and associated confidence intervals are further used to classify the factors into three groups: significant, insignificant, and non-risk factors. Subsequently, the Apriori algorithm is used to determine the interrelationship/association between the risk factors leading to a fatal crash outcome. Using the Apriori algorithm, a set of association rules involving three factors leading to a significant number of fatal crashes are identified. Finally, the derived results are combined to segment the factors associated with fatal crashes into six specific segments: Very high, High, Moderate, Low, Very low, and Extremely Low-risk factors. Such identification and segmentation of potential risk factors associated with fatal crashes would help the planning agencies to formulate mitigation measures for a low and medium-income country (LMIC) like India. [ABSTRACT FROM AUTHOR]

[Display omitted] This paper assessed the impact of five different WMA additives categorized under organic, chemical and foaming-based technologies on the energy requirement and green house gas (GHG) emissions. The maximum bearable cost, corresponding to different additives, was determined to assess the optimal choice of warm mix technologies. Results demonstrated that the addition of WMA additives lowers the heat energy by 3–12% compared to HMA. Although diesel is the most expensive fuel (considered in this study), its use can offset the cost of additives, for some WMA additives, namely: SR0.7, C0.2, C0.35, and R0.4. Calculations indicated that among the various warm mix additives, Sasobit Redux produces least emissions, followed by Aspha-min, Rediset, Sasobit, Cecabase, and viscosity graded binder. It was concluded that the use of WMA technologies can provide possible cost savings, and reduction in GHG emissions. [ABSTRACT FROM AUTHOR]

This paper contributes to the existing research on freight transportation, spatial and land use planning by investigating an improved spatial aggregation technique to delineate desirable freight traffic analysis zones. Zoning is a process of spatially aggregating several predefined basic spatial units (BSUs) into multiple zones. It plays a vital role in the transportation planning and decision-making process and is well-documented as the modifiable areal unit problem (MAUP). MAUP involves aggregating BSUs to obtain optimal zones satisfying specific criteria and constraints. This paper proposes an improved spatial aggregation methodology to develop a freight traffic analysis zone system by applying the multiobjective optimization concept using a genetic algorithm. The decision variables, namely, (i) Freight trip density; (ii) Number of establishments; (iii) Employment density; and (iv) Compactness, are chosen to represent the elements of freight, passenger traffic, and land use. The problem is formulated as a multiobjective network partitioning problem. The four objectives aim to create zones with better homogeneity and compactness. It is solved using a genetic algorithm with a weighted distance metric approach to prioritize the four objectives. Results show that zones resulting from the improved methodology are superior to the existing zones in terms of homogeneity of decision variables and compactness. The findings are expected to help the decision-making process of urban, transportation, and land-use planners in selecting appropriate freight traffic zone delineation strategies for a given region. • Multiobjective optimization concept is applied to develop freight traffic analysis zones (FTAZ) • Decision variables represent the characteristics of freight, passenger traffic, and land use. • Weighted distance metric approach using a genetic algorithm is used to prioritize zoning objectives • Findings suggest that FTAZs are superior to existing zones in homogeneity and compactness [ABSTRACT FROM AUTHOR]