Search Results (46)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

This study investigates the factors influencing school trip mode choice among senior high school students in the Chiang Rai urban area, Chiang Rai, Thailand, under normal and adverse weather conditions. Utilizing data from 472 students across six extra-large urban schools, a Multinomial Logit (MNL) regression model was applied to examine the effects of socio-demographic attributes, household vehicle ownership, travel distance, and spatial variables on mode selection. The results revealed notable modal shifts during adverse weather, with motorcycle usage decreasing and private vehicle reliance increasing, while school bus usage remained stable, highlighting its role as a resilient transport option. Car ownership emerged as a strong enabler of modal flexibility, whereas students with limited access to private transport demonstrated reduced adaptability. Additionally, increased waiting and travel times during adverse conditions underscored infrastructure and service vulnerabilities, particularly for mid-distance travelers. The findings suggest an urgent need for transport policies that promote inclusive and climate-resilient mobility systems, particularly in the context of Chiang Rai, including expanded school bus services, improved first-mile connectivity, and enhanced pedestrian infrastructure. This study contributes to the literature by addressing environmental variability in school travel behavior and offers actionable insights for sustainable transport planning in secondary cities and border regions. Full article

Building on our prior research with a national survey sample of 5385 US participants, the Pooled Rideshare Acceptance Model (PRAM) was built upon two factor analyses. This exploratory study extends the PRAM framework using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) to examine how 16 demographic variables influence and interact with the acceptance of Pooled Rideshare (PR), filling a gap in understanding user segmentation and personalization. Using a national sample of 5385 US participants, this methodological approach allowed for the evaluation of how PRAM variables such as safety, privacy, service experience, and environmental impact vary across diverse groups, including gender, generation, driver’s license, rideshare experience, education level, employment status, household size, number of children, income, vehicle ownership, and typical commuting practices. Factors such as convenience, comfort, and passenger safety did not show significant differences across the moderators, suggesting their universal importance across all demographics. Furthermore, geographical differences did not significantly impact the relationships within the model, suggesting consistent relationships across different regions. The findings highlight the need to move beyond a “one size fits all” approach, demonstrating that tailored strategies may be crucial for enhancing the adoption and satisfaction of PR services among various demographic groups. The analyses provide valuable insight for policymakers and rideshare companies looking to optimize their services and increase user engagement in PR. Full article

This study aims to improve the predictive accuracy of metropolitan planning organizations’ (MPOs’) travel demand models (TDM) by unraveling the factors influencing transportation mode choices. By exploring the interplay between trip characteristics, socioeconomics, built environment features, and regional conditions, we aim to address existing gaps in MPOs’ TDMs which revolve around the need to also integrate non-motorized modes and a more comprehensive array of features. Additionally, our objective is to develop a more robust predictive model compared to the current nested logit (NL) and multinomial logit (MNL) models commonly employed by MPOs. We apply a one-vs-rest random forest (RF) model to predict mode choices (Home-based-Work, Home-Based-Other, and non-home-based) for over 800,000 trips by 80,000 households across 29 US regions. Validation results demonstrate the RF model’s superior performance compared to conventional NL/MNL models. Key findings highlight that increased travel time and distance are associated with more auto trips, while household vehicle ownership significantly affects car and transit choices. Built environment features, such as activity density, transit density, and intersection density, also play crucial roles in mode preferences. This study offers a more robust predictive framework that can be directly applied in MPO TDMs, contributing to more accurate and inclusive transportation planning. Full article

Physical inactivity is a leading risk factor for non-communicable diseases. Climate change is now regarded as the biggest threat to global public health. Electric micromobility (e-micromobility, including e-bikes, e-cargo bikes, and e-scooters) has the potential to simultaneously increase people’s overall physical activity while decreasing greenhouse gas emissions where it substitutes for motorised transport. The ELEVATE study aims to understand the impacts of e-micromobility, including identifying the people, places, and circumstances where they will be most beneficial in terms of improving people’s health while also reducing mobility-related energy demand and carbon emissions. A complex mixed methods design collected detailed quantitative and qualitative data from multiple UK cities. First, nationally representative (n = 2000), city-wide (n = 400 for each of the three cities; total = 1200), and targeted study area surveys (n = 996) collected data on travel behaviour, levels of physical activity, vehicle ownership, and use, as well as attitudes towards e-micromobility. Then, to provide insights on an understudied type of e-micromobility, 49 households were recruited to take part in e-cargo bike one-month trials. Self-reported data from the participants were validated with objective data-using methods such as GPS trackers and smartwatches’ recordings of routes and activities. CO₂ impacts of e-micromobility use were also calculated. Participant interviews provided detailed information on preferences, expectations, experiences, barriers, and enablers of e-micromobility. Full article

Electric vehicles (EVs) are rapidly gaining popularity due to their environmental benefits, such as reducing greenhouse gas emissions. Considering the sociodemographic factors that influence the adoption of EVs is essential when developing equitable and efficient transportation policies. This article leverages the National Household Travel Survey (NHTS) 2022 data to analyze the sociodemographic factors influencing the adoption of EVs in the U.S. A binary logistic regression model and three machine learning models were employed to predict EV ownership in the U.S. The results of the regression model suggested that the Pacific division leads in EV adoption, most likely due to legislation and improved infrastructure, while regions such as East South Central suffer from lower EV adoption. The findings indicate that higher household income and home ownership significantly correlate with increased EV adoption. In contrast, renters and rural households exhibit lower adoption rates suggesting an increase in charging facilities in these regions can promote EV adoption. The Random Forest model outperforms others with an accuracy of 82.72%, suggesting its robustness in handling complex relationships between variables. Policy implications include the need for financial incentives for low-income households and increased charging infrastructure in rural and underserved urban areas to promote equitable EV adoption. Full article

CO₂ emissions from urban daily mobility play a major role in both environmental degradation, rising economic costs, and sustainability. Reducing these emissions not only advances environmental sustainability but also fosters economic development by enhancing public health, lowering energy consumption, and alleviating the financial strain caused by climate change. Understanding the dynamics of CO₂ emissions from urban daily mobility provides valuable historical insights into environmental impacts and economic costs tied to urban development. This study takes a historical perspective, examining changes in CO₂ emissions associated with daily mobility in the Lyon agglomeration across two decades, drawing on data from the 1995 and 2006 household travel surveys. Our findings reveal that individual factors such as gender, age, employment status, and income significantly influence CO₂ emissions, with males and full-time workers exhibiting higher emissions. Furthermore, household characteristics, including size and vehicle ownership, are critical in shaping emission levels, while urban form variables such as population density and mixed land use demonstrate a negative correlation with emissions, highlighting the importance of urban planning in mitigating CO₂ output. The analysis emphasizes that greater accessibility and proximity to essential services are vital in reducing individual emissions. Based on these insights, we discuss the implications for policy design, suggesting targeted strategies to enhance urban mobility, improve public transport accessibility, and promote sustainable urban development. Finally, we outline directions for future research to further explore the intricate relationship between urban characteristics and emissions, ultimately aiming to contribute to the development of effective climate policies. Full article

Using survey data from three dwelling types in Jakarta, we examine how dwelling type, socioeconomic identity, and commuting distance affect women’s travel-mode choices and motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) model with two machine-learning classifiers, random forest (RF) and XGBoost, using Shapley additive explanations (SHAP) for interpretation. The models’ efficacy varies across different datasets, with XGBoost mostly outperforming other models. The women’s preferred commuting modes varied by dwelling type and trip purpose, but their motives for choosing the nearest activity were similar. Over half of the women rely on private motorized vehicles, with women living in the gated community heavily relying on private cars. For nearby shopping trips, low income and young age discourage women in urban villages (kampungs) and apartment complexes from walking. Women living in gated communities often choose private cars to fulfill household responsibilities, enabling them to access distant options. For nearby leisure, longer commutes discourage walking except for residents of apartment complexes. Car ownership and household responsibilities increase private car use for distant options. SHAP analysis offers practitioners insights into identifying key variables affecting travel-mode choice to design effective targeted interventions that address women’s mobility needs. Full article

Solar-based home PV systems are the most amazing eco-friendly energy innovations in the world, which are not only climate-friendly but also cost-effective solutions. The tropical environment of Malaysia makes it difficult to adopt photovoltaic (PV) systems because of the protracted rainy monsoon season, which makes PV systems useless without backup batteries. Large quantities of lithium-ion battery (LIB) trash are being produced by the electric vehicle (EV) sector. A total of 75% of the highest capacity levels have been discarded. By 2035, it is predicted that the wasted LIBs held as a result of expensive recycling and difficult material separation would carry up to 1200 GWh. An economical and sustainable option is offered by our study, which prototypes a replicated LIB pack that is incorporated into a PV home system. This study investigates the transformational power of second-life electric vehicle batteries (SLEVBs) when incorporated into home photovoltaic (PV) systems. The concept entails reusing existing electric vehicle batteries for stationary applications, offering a unique approach to extending the life of these batteries while meeting the growing need for sustainable domestic energy storage. The study looks at the technological feasibility, economic viability, and environmental effect of introducing SLEVBs into household PV systems, giving vital insight into their role in revolutionizing energy storage techniques and promoting sustainability. In comparison to the Lead–Acid Battery (LAB) system, the SLEVB system has a cheaper total cost of ownership, with savings of 12.62% compared with new LABs. A CO₂ emission reduction of at least 20% is achieved by using the SLEVB system compared with LABs. Electricity can be provided in houses in rural areas where there is no electricity. As a result, the security and superiority of the life of rural residents will improve. It is anticipated that the suggested strategy will lower EV pricing, enabling EV adoption for M40 and B40 groups. Consequently, the Malaysian and worldwide EV business will remain viable. Full article

This study aims to evaluate the potential of electric vehicle (EV) carsharing services to address social and environmental disparities in urban transportation through an evaluation of BlueLA, a one-way station-based carsharing service in Los Angeles, California. BlueLA provides a clean and affordable mobility option in underserved communities that face significant air quality burdens and have historically been excluded from environmental benefits. By incorporating BlueLA trip activity data from January 2021 to December 2022 (n = 59,112 trips) and an online user survey implemented in early December 2022 (n = 215 responses), we estimate the impacts of BlueLA on personal vehicle ownership patterns, vehicle miles traveled (VMT), and associated greenhouse gas (GHG) emissions. The results show an overall net reduction in VMT and GHG emissions of 463,845 miles and 656 metric tons, respectively, among the BlueLA user population (3074 registered users). When disaggregating impacts by BlueLA member type, our findings show a net reduction of 234 and 371 metric tons in GHG emissions for Standard (general population) and Community (low-income qualified) members, respectively. Additionally, our socio-demographic analysis highlights clear disparities between these two member groups, with Community members typically having lower incomes (i.e., 74% earning below USD 50,000 annually); lower educational attainment (i.e., 46% with at most an associate’s degree); and larger households (i.e., 23% living in households of four or more) compared to Standard members (i.e., 19% earning below USD 50,000, 24% with at most an associate’s degree, and 9% in households of four or more). Moreover, when comparing the VMT and associated GHG emissions due to BlueLA, we find that the presence of BlueLA reduces VMT and GHG emissions by 34% and 48% respectively, and each BlueLA vehicle replaces 16 personally owned vehicles (shed and postponed purchases). Last, when comparing the emissions produced by the electric fleet of BlueLA to those of a comparable fleet of internal combustion engine vehicles, we find that the use of an EV fleet reduces GHG emissions by 43% in comparison. The BlueLA carsharing service has led to notable net reductions in VMT and thus GHG emissions, with a major share of these reductions observed among Community members. Full article

The emergence of automated mobility-on-demand (AMoD) services in urban regions has underscored crucial issues concerning the sustainable advancement of urban mobility. In particular, the impact of various AMoD implementation strategies in dense, transit-oriented cities has yet to be investigated in a generalized manner. To address this gap, we quantify the effects of AMoD on trip patterns, congestion, and energy and emissions in a dense, transit-oriented prototype city via high-fidelity simulation. We employ an activity- and agent-based framework, with specific demand and supply considerations for both single and shared AMoD rides. Our findings suggest that, in densely populated, transit-oriented cities such as the Tel Aviv metropolis, AMoD contributes to higher congestion levels and increased passenger vehicle kilometers traveled (VKT). However, when AMoD is integrated with public transit systems or introduced alongside measures to reduce household car ownership, it helps alleviate the VKT impact. Furthermore, these combined approaches effectively counter the negative impact of AMoD on public transit ridership. None of the AMoD strategies analyzed in our study reduce the congestion effects of AMoD and all strategies cannibalize active mobility in dense, transit-oriented cities compared to the base case. Nevertheless, our analysis reveals that a policy leading to decreased car ownership proves to be a more efficient measure in curbing energy consumption and greenhouse gas emissions. Full article

Amman, the capital of Jordan, has experienced significant traffic congestion due to the rise in private vehicle ownership and limited public transportation services. A Stated Preference (SP) survey was conducted to determine public transportation users’ willingness to use the Bus Rapid Transit (BRT) service. Another survey assessed the demand for an on-demand transit bus service with flexible and moderate costs, particularly for individuals far from the main BRT stations who need to reach them. Two models, Multinomial Logit (MNL) and Mixed Logit (ML), were utilized to understand user preferences for work-related trips. The study findings indicate that the cost of the trip and the waiting time are the two primary factors influencing public transport users’ choices. Furthermore, sociodemographic factors, such as age, income, household size, and current status, were found to have a significant impact. The results reveal that approximately 71% of participants would utilize an integrated public transportation system comprising BRT and on-demand services. The findings underscore the potential benefits of an integrated public transport system in addressing Amman’s traffic congestion. By combining BRT and on-demand services, the city can offer residents comfortable, affordable, and efficient transportation options, thus effectively mitigating congestion. Full article

Sustainable land management (SLM) practices are often touted as a vehicle for simultaneously increasing agricultural productivity and food security in rural areas. In Eastern Zambia, numerous initiatives such as the Zambia Integrated Forest Landscape Project (ZIFLP) have been implemented. Yet, empirical data suggest relatively low levels of SLM uptake in the smallholder farming sector. Therefore, the broad objective of this study was to estimate the relationship between smallholder farmer awareness of SLM technologies and land allocated to SLM at the farm level. We hypothesized the following: H1: Increased farmer awareness of SLM practices leads to more land allocated to SLM activities in Zambia’s Eastern Province; and H2: Adoption of specific SLM practices influences the extent of land allocated to SLM. Using an intra-household cross-sectional survey, data were collected from 761 randomly selected households from 11 chiefdoms of the Eastern Province. The Heckman selection procedure was used to analyze the study’s overarching hypothesis. Findings showed that farmers were generally conversant with SLM as a construct (>90%), with choices being influenced by gender. Conservation agriculture in the form of crop rotations, use of manure, mixed cropping, tree planting, and minimum tillage methods were the most commonly known SLM technologies among farmers. Findings also indicated that awareness is an important antecedent in the use of SLM practices (χ² = 76.6, p = 0.00), with greater access to extension being positively associated with farmer awareness (p < 0.05). The land allotted to SLM hinged on crop diversity, ownership of different types of livestock, and access to agricultural extension. These findings suggest that long-term commitments to training farmers in SLM is critical. This will be achieved when there is coherence in the information on SLM being given to farmers by all the actors working in the region. Full article

Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as “Have-it-alls”, who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth (“nesting period”), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as “Couples”, who start careers and partnerships early but delay parenthood, and “Singles”, who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention. Full article

A growing population and increasing consumer demand have created unprecedented pressures on the environment and natural resources. The private vehicles market, one of the largest markets in the world, is associated with considerable environmental costs. Sharing electric vehicles, where consumers can enjoy the benefits of a greener vehicle without owning it, has emerged as an innovation that can reduce some of the environmental costs of ownership. However, uncertainty around the determinants of participation remain. This study employs an econometric model using survey and experimental data that were collected at the initial stages of the roll-out of an electric-vehicle-sharing service in Malta, in order to identify the psychological factors that determine the willingness to use and to pay for such a service, the propensity to walk to a car-sharing station, as well as the likelihood of scrapping a privately owned vehicle. The findings suggest that engagement in the car-sharing market is more likely to take place among those who have a lower psychological attachment to the private car, are already using multiple transport methods and are sharing a car with other household members. A large number of cars per household and a high use are negatively associated with uptake. The results also suggest that consumers who care about the environment are more likely to engage in car sharing. Full article