Commute Costs Research Articles

The Impacts of Remote Work and Attitudinal Shifts on Commuting Reductions in Post-COVID Melbourne, Australia

This paper analyses the commuting frequencies and modal choices of travellers in Melbourne, using a dataset reflecting travel behaviour before and after COVID-19. A factor analysis of 63 latent variables identified seven key factors, which were used in cluster analysis to examine the relationships between latent constructs, land use, and socio-demographic variables, as well as commuting behaviours. The analysis categorised white-collar employees into four groups based on their remote work engagement, with socio-demographics and industry type as key factors. The analysis shows that female clerical and administrative workers who worked from home during the pandemic are now returning to the office, raising gender equality concerns within society. Meanwhile, the education and training sector mandates office attendance despite the feasibility of remote work, as universities prioritise in-person attendance to attract more international students, impacting societal norms around telecommuting. The analysis revealed that saving on commute costs, reducing travel time, and spending more time with family are the among the primary factors influencing travel behaviour among white-collar employee’s post-pandemic. The study found that the decrease in public transport trips is associated with increased telecommuting rather than service dissatisfaction, especially among Central Business District (CBD) employees who still rely on public transport. This trend suggests that the CBD sector’s growing acceptance of remote work is reducing daily commutes, which puts additional pressure on public transport providers to sustain and improve their services. A decline in service quality could further reduce ridership, highlighting the need for consistent, high-quality public transport. Furthermore, the study found that increased telecommuting is likely to reduce car trips in the future, especially among healthcare and social workers who prefer driving due to public transport’s unreliability for their demanding schedules. By examining variables like the advantages and disadvantages of working from home, convenience, accessibility, and the efficiency of public transport, this study enhances the understanding of transport behaviour and underscores the need to improve public transport reliability to support sustainable cities as remote work grows.

ViLT-CLIP: Video and Language Tuning CLIP with Multimodal Prompt Learning and Scenario-Guided Optimization

Pre-trained vision-language(V-L) models such as CLIP have demonstrated impressive Zero-Shot performance in many downstream tasks. Since adopting contrastive video-text pairs methods like CLIP to video tasks is limited by its high cost and scale, recent approaches focus on efficiently transferring the image-based CLIP to the video domain. A major finding is that fine-tuning the pre-trained model to achieve strong fully supervised performance leads to low zero shot, few shot, and base to novel generalization. Instead, freezing the backbone network to maintain generalization ability weakens fully supervised performance. Otherwise, no single prompt tuning branch consistently performs optimally. In this work, we proposed a multimodal prompt learning scheme that balances supervised and generalized performance. Our prompting approach contains three sections: 1) Independent prompt on both the vision and text branches to learn the language and visual contexts. 2) Inter-modal prompt mapping to ensure mutual synergy. 3) Reducing the discrepancy between the hand-crafted prompt (a video of a person doing [CLS]) and the learnable prompt, to alleviate the forgetting about essential video scenarios. Extensive validation of fully supervised, zero-shot, few-shot, base-to-novel generalization settings for video recognition indicates that the proposed approach achieves competitive performance with less commute cost.

Tax Treatment of Commuter Cost

Tax Treatment of Commuter Cost

Urban Sprawl Patterns, Drivers, and Impacts: The Case of Mogadishu, Somalia Using Geo-Spatial and SEM Analyses

There is a lack of research on urban sprawl in developing countries, particularly in Sub-Saharan Africa, undergoing significant demographic change. There is an urgent need to conduct more studies on African cities and investigate spatial variations in urban sprawl to fill a knowledge gap in Sub-Saharan Countries (SSC). There have been no studies of urban sprawl in the Somali capital of Mogadishu, a fragile metropolis struggling with the legacy of decades of civil war. This study has two main objectives: (i) to examine sprawl patterns in Mogadishu, Somalia; and (ii) to identify the drivers and impacts of urban sprawl in Mogadishu, Somalia. The study used spatiotemporal imagery from 2006, 2013, and 2021 to identify sprawl patterns. A quantitative method in the form of a cross-sectional survey with 265 participants was then used to identify the drivers and impacts of sprawl, which was then analysed using the structural equation model (SEM). The spatiotemporal analysis results showed sprawl patterns in nine districts and three settlements, mainly scattered and leapfrog patterns. The SEM discovered five significant drivers: low price of land and dwelling (LP), development of transportation infrastructure (DTI), rising income, security reasons, and low commute cost (LCC), in addition to eight significant impacts: less social interaction (LSI), agriculture land and natural habitat loss (AGL NHL), unsafe environment (USE), insufficient health and educational services (IHF IEF), high public services cost (HPSC), insufficient public transport (IPT), less physical activity (LPA), pollution (POL) and mental health issues (MH). Undoubtedly, the impacts found in the study proved that urban sprawl negatively impacted the residents and environment of Mogadishu, which will continue as the security situation in the city improves and more residents are attracted.

Mobility prediction for uneven distribution of bikes in bike sharing systems

SummaryUser mobility represents the movement of either individual or group. In smart cities, detection and prediction of mobility patterns are required for numerous applications like resource distribution, traffic management, and user behavioral analysis. With the increase in the number of smart vehicles, urban mobility detection and prediction have become a critical problem for study. Bike‐sharing ecosystems (BSS) form an integral part of such ecosystems, as it supports the green revolution, ease of access, and solves traffic problems. However, recent schemes have suggested that BSS are challenged by issues of high density, mobility complexity of bikes (stations), large commute cost, uneven distribution, and route imbalances. To address the critical issues, the article proposes a hybrid scheme that combines rebalancing using clustering that addresses the mobility complexity. Once rebalancing is done, we address the uneven distribution among clusters using prediction models. This article is presented a comparative analysis of algorithms like fuzzy C‐means clustering, linear regression, decision tree, and random forest classifiers for predictive analysis performed on weather data and nonweather data. The presented results indicate the viability of the proposed model in real‐world scenarios.

The use of E-moped increases commute satisfaction and subjective well-being: Evidence from Shanghai, China

Increasing residents' commute satisfaction and subjective well-being contribute to a healthy, sustainable, and successful society. In this respect, transport mode choice is commonly recognized as one of the key factors associated with commute satisfaction and commuters' subjective well-being. However, few studies have investigated how e-moped, one of the most popular commute transport modes in China, shapes residents' commute satisfaction and subjective well-being, although e-moped ownership in China is extremely great. Thus, we conducted an empirical study on the impact of commuting by e-mopeds, using the survey data from eight residential communities in Shanghai. A path analysis model was established to investigate the relationships between commute characteristics (including commute distance, commute time, commute costs, transport mode choice, transport environment), socio-demographics, commute satisfaction, and subjective well-being. The model results showed that commute satisfaction positively correlated with commuters’ subjective well-being. However, the relationship between transport mode choice and commute satisfaction was demonstrated not straightforward but fully mediated by commute time and commute costs which both showed a negative impact on commute satisfaction. In this case, using active modes was revealed to lead to a happier work commute than using private cars, whereas bus riders were at the bottom of the list as expected. Surprisingly, the use of e-mopeds resulted in the second highest total effects on the increase of commute satisfaction and subjective well-being, due to less commute time and lower commute costs. Moreover, the substitution effects of e-mopeds were examined. The results indicated that bus commuters held the most possibility to shift to e-mopeds; while, it was found hard to let e-moped users alter to the other transport modes of commuters. In this context, it was reasonable to further promote the development of e-mopeds in Shanghai, and targeted practical implications were given in this study.

UNCERTAINTY IN PROCUREMENT CONTRACTING WITH TIME INCENTIVES

AbstractThis article studies cost‐plus‐time (A+B) procurement contracting with time incentives in the highway construction industry. In the presence of construction uncertainty, the contractor's actual completion time may deviate from the bid completion time, and the A+B contract design is not ex post efficient. Using data from highway procurement contracts in California, we show that an ex post efficient lane rental contract would reduce the social cost by $41.39 million (43.11%) on average. Moreover, the average commuter cost would decrease by $62.06 million (78.96%), suggesting a substantial reduction in the construction externality to commuters from lane rental contracts.

City structure shapes directional resettlement flows in Australia

Modern urban science views differences in attractiveness of residential suburbs as the main driver of resettlement within a city. In particular, certain suburbs may attract residents due to lower commute costs, and this is believed to lead to compactification of a city, with highly populated central business district and sprawled suburbia. In this paper we assess residential resettlement patterns in Australian capital cities by analyzing the 2011 and 2016 Australian Census data. Rather than explicitly defining a residential attractiveness of each suburb in subjective terms, we introduce and calibrate a model which quantifies the intra-city migration flows in terms of the attractiveness potentials (and their differences), inferring these from the data. We discover that, despite the existence of well-known static agglomeration patterns favouring central districts over the suburbia, the dynamic flows that shape the intra-city migration over the last decade reveal the preference directed away from the central districts with a high density of jobs and population, towards the less populated suburbs on the periphery. Furthermore, we discover that the relocation distance of such resettlement flows plays a vital role, and explains a significant part of the variation in migration flows: the resettlement flow markedly decreases with the relocation distance. Finally, we propose a conjecture that these directional resettlement flows are explained by the cities’ structure, with monocentric cities exhibiting outward flows with much higher reluctance to long-distance relocation. This conjecture is verified across the major Australian capitals: both monocentric (Sydney, Melbourne, Brisbane, Adelaide, Perth, Hobart) and polycentric (Darwin and Canberra).

Hierarchy Divisions of the Ability to Endure Commute Costs: An Analysis based on a Set of Data about Construction Workers

Hierarchy Divisions of the Ability to Endure Commute Costs: An Analysis based on a Set of Data about Construction Workers Keren Sun, Peter Philips, Xin Zhao Abstract This paper proposes a new hierarchy theory, i.e., hierarchy divisions of the ability to endure commute costs, as the alternative theory of Transportation System Users’ Hierarchy of Needs. We analyze the structure of commute costs, commute costs consist of safety cost, time cost, societal cost, money cost, and cost of comfort and convenience, which include subjective costs and objective costs. we have simplified the commute cost as a function of commuting distance. As the abilities to acceptance of commuting distance for construction workers are mainly relative to their wage levels, so we choose wages per hour and commute distance as clustering variables to do clustering analysis of construction industry data, and get some new views, such as, from the overall perspective of construction workers, the relationship between the wage level and the ability that can endure commute cost is positive, and a large proportion of construction workers can only or only willingly endure low commute cost; from the ethnicity perspective of construction workers, we can know that the abilities of construction workers of different ethnicity to endure commuting costs are different, which rank as follows: African American> White> American Indian> Asian or Pacific Islander> Other> Hispanic> Mixed races; from the occupation perspective of construction workers, we can know that the abilities of construction workers of different occupation to endure commuting costs are different, which rank as follows: Roofer > Ironworker > Sheetmetal > Operating engineer > Laborer > Apprentice > Electrician > Journeyman > Plaster Drywall > Foreman > Carpenter > Mason> Plumber fitter, etc. Full Text: PDF DOI: 10.15640/jeds.v8n4a1

Seeking protection from precarity? Relationships between transport needs and insecurity in housing and employment

The importance of the nexus between transport, housing location and employment location has long been identified as important to social welfare. In transport research this has however operated largely at either end of the spectrum of advantage. There exists a strong tradition, with roots in welfare economics, which explores those with choices and how they make trade-offs between where to live and work, the associated wage rate and the commute costs. At the other end is work which recognises the social costs for those that do not have access to transport and struggle to participate in employment.This paper focuses its attention on households that fall between these extremes and for whom the choice/no-choice dichotomy does not work. Through in-depth interviews with 46 people in the UK we find that the interactions between the location and, critically, security of both housing and employment plays a critical role in shaping what ‘choices’ exist. In particular, the findings explain why some households own cars although, on other metrics, they would not be expected to find ownership affordable, and how the security of housing tenure shapes long-term household trajectories. The literature on planning and travel behaviour has paid little or no attention to the security of housing and employment. This study suggests the importance of addressing this gap and refocussing attention on the different ways in which transport connects to wider planning and social policy.

Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations

Autonomous electric vehicles have attracted enormous interests as an effective way to significantly improve urban transportation efficiency, reduce commute cost and the corresponding environmental burden. This work proposed a multiphysics energy model to quantify the energy consumption and greenhouse gas emissions from an autonomous electric vehicle based on vehicle dynamics and the vehicle system energy demand. A case study is conducted on a mid-size autonomous electric vehicles taxi operating in New York City based on possible driving data and scenarios. It is found that the monthly average unit energy consumption for the autonomous electric vehicle ranges from 325 to 397 Wh km−1, and the greenhouse gas emissions is 6.5% more from an autonomous electric vehicle with a driver than that without a driver. The study provides a physical approach for quantifying the energy consumption and greenhouse gas emissions from an autonomous electric vehicle, and can support the sustainable development and deployment of autonomous electric vehicle technologies in future.

Commuting for women in Saudi Arabia: Metro to driving - Options to support women employment

Saudi Arabian women traditionally have been dependent on male relatives, hired drivers, or private transportation to get to work as they were not permitted to drive until June 2018. Some believe this has created a barrier for those women who wanted to enter the workforce. This research was conducted to determine whether accessibility (cost and time) for different types of transport has a relationship with women's opportunity to work. The unemployment rate for Saudi women in 2016 was nearly six times that of Saudi men. Qualitative evidence suggests the high cost of private transportation is a limiting factor for women working in Riyadh (Bashraheel, 2009; Jiffry, 2012). However, studies have yet to quantify the relationship between the location of employment, the job participation rate, and commute costs. By using a commuter accessibility model based on the financial cost of commuting for four female employment sectors—manufacturing, retail, healthcare, and education—this research sets out to test the relationship between commute costs and employment for Riyadh women. The study, which provides the first comparative commute cost maps for Riyadh, looks at commute costs for driving alone, private drivers, street-hailed and app-based taxi services, and the new Metro system. The results show that when commute costs increase, employment among women decreases. This means that reducing commute costs, perhaps by allowing women to drive themselves to work, increases the opportunity for women to work. The research also showed that manufacturing is the least accessible sector for women and would benefit from new forms of transit such as car-pooling.

A carpool matching model with both social and route networks

Carpooling is an effective solution for traffic congestion. However, social obstructions (lack of trust) and cost obstructions (high commute cost) are still two major challenges for promoting carpool activities. In this paper, a social-route-network carpooling model (SRNC model) is proposed. The model combines commuters' social networks and commute route networks to provide optimization on both trust and cost in different carpooling teams. The trust optimization improves the social comfort between commuters by introducing the concept of degrees-of-separation and the user preference. The cost optimization reduces the total commute distance with integer programming model. Finally, both trust and cost are balanced in a unified model—the SRNC model. By using the Twitter social network and the Washington, D.C. road network, the SRNC model is evaluated. Results demonstrate a significantly improvement of the trust (around decuple) with a small loss in route cost (around 6.3%) as compared to a route-network-only carpooling model.

Urban Sprawl and Land Speculation in Contemporary Land Administration in Bauchi Nigeria

Urban sprawl and land speculation in Bauchi is ever on the rise and posed problems like high cost of infrastructure, farmland invasion, high commuter cost as well as real estate unattractively developed in patchy and strung out manner. This study investigates the impact of urban sprawl and land speculation on land administration in Bauchi metropolis, using multiple regression analysis and Partial Least Squares (SmartsPLS). Both urban sprawl and land speculation significantly influenced land administration, but land speculation alone tremendously influenced land administration as it accounts for 82% of the variance in the endogenous variable. Two-rate property tax is therefore recommended for curbing land speculation in the study area.

Residential Location Choice in the Era of Shared Autonomous Vehicles

This study examines the potential changes in residential location choice in a scenario where shared autonomous vehicles (SAVs) are a popular mode of travel in the Atlanta metropolitan area. This hypothetical study is based on an agent-based simulation approach, which integrates residential location choice models with a SAV simulation model. The coupled model simulates future home location choices given current home location preferences and real estate development patterns. The results indicate that commuters may relocate to neighborhoods with better public schools and more amenities due to reductions in commute costs.

Commute costs and labor supply: evidence from a satellite campus

Whether, and how much, increased commute costs decrease labor supply is important for transport policy, city growth, and business strategies. Yet empirical estimates are limited and biased downward due to endogenous choices of residences, workplaces, commute modes, and wages. We use the transition of undergraduate teaching from a Chinese university’s urban to suburban campus and ten years of complete course schedule data to test how teachers’ labor supply responds to a longer commute. Exogeneity is ensured because few faculty change residences, nearly all faculty use a free shuttle service, and we control for wage changes. Comparing before and after, the 1.0 to 1.5-hour (40-kilometer) increase in round-trip commute time reduces annual undergraduate teaching by 56 hours or 23%. Consistent with higher per-day commute costs annual teaching days decrease by 27 while daily teaching hours increase by 0.49. Difference-in-difference estimates using faculty-specific changes in commute time corroborate these results ruling out aggregate confounders. Faculty substitute toward graduate teaching but decrease research output. The university accommodated the reduced teaching time primarily by increasing class sizes implying that education quality declined.

How the Poor Commute in Bangkok, Thailand

This study examined how the poor traveled to work in Bangkok, Thailand. Two sets of travel surveys were conducted in groups categorized as low income: residents in low-income communities and low-paid workers in various job locations. The results confirmed the general understanding that travel behaviors of the poor depended not only on income but also on public transport availability, job location, and home community location. Suburban residents with limited public transport services tended to travel by private motorcycles and to spend a large portion of household transport expenditures on vehicle and fuel costs. Others who lived near public transport services depended more on public transport and spent a larger share of transport expenditures on fares. No significant difference in household transport expenditures between the two groups was found. The travel survey by occupation revealed that privately owned motorcycles were the most popular mode, followed by employer-provided transport and public buses. Informal modes constituted a small share because of the relatively high fares. The average commute costs of informal modes were higher than the average cost of public buses and were closer to the average cost of private motorcycles. Male commuters were more dependent on private motorcycles and less on public transport than female commuters. With age, dependence on private motorcycles increased and dependence on public transport decreased. Average commute times did not vary significantly among occupational groups. These results highlighted the importance of low-cost public transport to vulnerable subgroups of the urban poor.

Urban spatial structure and commute duration: An empirical study of China

ABSTRACTUrban traffic is embedded in and fundamentally shaped by the spatial pattern of urban land use, such as city size, density, extent of polycentricity, and the relationship between employment and residential locations. Previous evidence, mainly from European and American cities, suggests that the duration of commute trips increases with city size and the spatial separation between jobs and housing. On the other hand, the influences of density and polycentricity are less clear. Using data from 164 cities in China, this study empirically analyzes the relationship between city average commute duration and multiple dimensions of urban spatial structure. Controlling for economic, demographic, and infrastructure characteristics, we find that commute duration correlates positively with city size and jobs–housing separation but negatively with density and polycentricity. As one of the earliest studies on commute cost in the rapidly urbanizing and motorizing Chinese cities, this study can help Chinese decision makers improve urban economic and environmental efficiency through spatial planning and policy making. Specifically, compact, mixed-use, and polycentric spatial development may ease the burden of commute, and thus substitute for unnecessary infrastructure investment and energy consumption during a period of rapid urban expansion in China.

The effect of Beijing's driving restrictions on pollution and economic activity

We evaluate the pollution and labor supply reductions from Beijing's driving restrictions. Causal effects are identified from both time-series and spatial variation in air quality and intra-day variation in television viewership. Based on daily data from multiple monitoring stations, air pollution falls 21% during one-day-per-week restrictions. Based on hourly television viewership data, viewership during the restrictions increases by 9 to 17% for workers with discretionary work time but is unaffected for workers without, consistent with the restrictions' higher per-day commute costs reducing daily labor supply. We provide possible reasons for the policy's success, including evidence of high compliance based on parking garage entrance records.

Identificación de los factores del coste social del transporte: una revisión de la literatura

The prevailing model of transport characterized by a predominant use of private vehicles, identified the need to develop a policy transport towards sustainability in the European Union. This policy marked the Spanish Strategy for Sustainable Mobility, aimed at promoting more sustainable means - i.e. “public transport”, car-sharing”- to reduce emissions and energy consumption. With these objectives it was launched in 2013 the Co-Mobility (TIN2012-31104) project, funded by the Ministry of Economy and Competitiveness which seeks to apply new technologies to improve the mobility of people on their commute. As one of the objectives of this project is to identify the transport costs to alert the commuter to a more sustainable mobility, this paper highlights an approach to the key variables for commute costs calculator by a detailed review of the existing literature in order to justify the variables and quantities included in the commuting costs.