A social-ecological model of working from home during COVID-19

Working from Home (WFH) is emerging as a critical measure for reducing transport demand. Indeed, the COVID-19 pandemic has revealed that trip avoidance measures, especially WFH, could help address Sustainable Development Goal 11.2 (creating sustainable transport systems in cities) by decreasing commuter trips by private motor vehicles. This study aimed to explore and identify the attributes that supported WFH during the pandemic and construct a Social-Ecological Model (SEM) of WFH within the context of travel behaviour. We conducted in-depth interviews with 19 stakeholders from Melbourne, Australia and found that WFH during COVID-19 has fundamentally changed commuter travel behaviour. There was a consensus among participants that a hybrid-work model will emerge post-COVID-19 (e.g., working three days in the office and two days at home). We identified 21 attributes that influenced WFH and mapped these attributes across the five traditional SEM levels (intrapersonal, interpersonal, institutional, community and public policy). In addition, we proposed a sixth higher-order level: "global", to reflect the worldwide phenomena of COVID-19 and computer programs that also supported WFH. We found that WFH attributes were concentrated at the intrapersonal (individual) and institutional (workplace) levels. Indeed, workplaces are key to supporting WFH in the long-term. Whereby, workplace provision of laptops, office equipment, internet connection and flexible work policies enable WFH, and unsupportive organisational cultures and managers are potential barriers to WFH. This SEM of WFH benefits both researchers and practitioners by providing guidance of the key attributes required to sustain WFH behaviours post-COVID-19.

Who is returning to public transport for non-work trips after COVID-19? Evidence from older citizens' smart cards in the UK's second largest city region

Harnessing a unique data source - longitudinal travel smartcard data linked to passenger demographics from 2019 to 2022 - we use methods of survival analysis to model the recovery of public transport patronage among 183,891 senior citizens resident in the West Midlands metropolitan region in the United Kingdom. Comparing pre and peri-pandemic patronage, we identify pronounced social and spatial inequalities in the speed of return to public transport. We find that male, younger and non-White passengers are more likely to return to public transport as soon as movement restrictions were lifted, whereas passengers from White ethnic background and affluent areas do not return to public transport within the first year after the outbreak. Pronounced social inequalities persist into the middle of 2021, and only thence they began to attenuate as part of a wider return to public transport among passengers post retirement age. In 2022, 80% of these passengers have returned to public transport but the frequency of use has remained lower than prior to the pandemic. We discuss implications for transport policy and planning.

The shareability potential of ride-pooling under alternative spatial demand patterns

In In this study, we set out to explore how various spatial patterns of travel demand drive the effectiveness of ride-pooling services. To do so, we generate a broad range of synthetic, yet plausible demand patterns. We experiment with the number of attraction centres, the dispersion of destinations around these centres, and the trip length distribution. We apply a strategic ride-pooling algorithm across the generated demand patterns to identify shareability potential using a series of metrics related to ridepooling. Our findings indicate that, under a fixed demand level, vehicle-hour reduction due to ride-pooling can range between 18 and 59%. These results depend on the concentration of travel destinations around the centre and the trip length distribution. Ride-pooling becomes more efficient when trips are longer and destinations are more concentrated. A shift from a monocentric to a polycentric demand pattern is found to have a limited impact on the prospects of ride-pooling.

COVID-19 and suburban public transport in the conditions of the Czech Republic

This article focuses on possible approaches to safe regional public transport during the COVID-19 pandemic. The purposes of the research are examination the conditions for ensuring safe transport and the impact on the planning of transport services. The result is an assessment of the operation of regional public transport, consisting of the possibility of maintaining safe distances in public transport. Authors work on suburban transport cases in selected regions of the Czech Republic (Prague and Moravian-Silesian Region). Census devices in public transport, periodical transport surveys, Google mobility reports and data on fare sales from regional transport were used as data sources. Emphasis is placed on a safe distance between commuters, this condition leads to lower occupancy of the vehicle while maintaining the capacity of the vehicles. The value of this new occupancy is determined for selected vehicles and the coefficient that represents the maximum occupancy level to ensure safe transport is established. The capacity of the connections is examined in the period before and during the COVID-19 pandemic. Compared to the period before COVID-19, the daily variation of passengers is expected to change significantly, leading to different occupancy rates during the day.

Open synthetic travel demand for Paris and Île-de-France: Inputs and output data

Hörl and Balac [1] describe a data pipeline to generate a synthetic travel demand for Paris and Île-de-France. The data set consists of households, persons and their daily activity chains. It can be used in transport simulation, energy analysis and other research fields such as epidemiology. This data-in-brief article describes in detail the generated data set and how it can be regenerated based on publicly available and open data. The characteristics and pre-processing steps for the input data sets are covered in detail.

Should bike-sharing continue operating during the COVID-19 pandemic? Empirical findings from Nanjing, China

INTRODUCTION

Coronavirus disease 2019 (COVID-19) has triggered a worldwide outbreak of pandemic, and transportation services have played a key role in coronavirus transmission. Although not crowded in a confined space like a bus or a metro car, bike-sharing users are exposed to the bike surface and take the transmission risk. During the COVID-19 pandemic, how to meet user demand and avoid virus spreading has become an important issue for bike-sharing.

METHODS

Based on the trip data of bike-sharing in Nanjing, China, this study analyzes the travel demand and operation management before and after the pandemic outbreak from the perspectives of stations, users, and bikes. Semi-logarithmic difference-in-differences model, visualization methods, and statistic indexes are applied to explore the transportation service and risk prevention of bike-sharing during the pandemic.

RESULTS

Pandemic control strategies sharply reduced user demand, and commuting trips decreased more significantly. Some stations around health and religious places become more important. Men and older adults may be more dependent on bike-sharing systems. The declined trips reduce user contacts and transmission risk. Central urban areas have more user close contacts and higher transmission risk than suburban areas. Besides, a new concept of user distancing is proposed to decrease transmission risk and the number of idle bikes.

CONCLUSIONS

This paper is the first research focusing on both user demand and transmission risk of bike-sharing during the COVID-19 pandemic. This study evaluates the mobility role of bike-sharing during the COVID-19 pandemic, and also provides insights into curbing the viral transmission within the city.

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach

Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people's social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it's found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people's demands. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people's travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision-makers can contribute to the improvement of the performance of combined non-pharmaceutical interventions, and it also benefits the short-term and long-term interventions in the future.

Enhancing non-motorist safety by simulating trip exposure using a transportation planning approach

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis.

Telework and daily travel: New evidence from Sweden

Telework, long promoted as a way to reduce daily travel and address congestion problems, has been extensively studied in transport research. Empirical consensus has long held that telework reduces overall travel, but several updated studies now suggest the opposite. Meanwhile, telework has steadily increased in many countries, and few studies have examined contexts where regular teleworkers have grown to form an early majority. We study how telework influences daily travel in such a context, namely, Sweden from 2011 to 2016. Using representative micro-data from the Swedish National Travel Survey, this study also captures travel behaviour during the defined period when the telework was actually practiced, distinguishing different telework arrangements and analysing a range of travel behavioral outcomes. We conclude that telework leads to reduced travel demand, more use of active transport modes, and congestion relief. Important differences between full- and part-day teleworkers are also highlighted, stressing the importance of understanding telework as a diversified coping strategy for organizing the spatiotemporality of everyday life.

Study of multi-objective path planning method for vehicles

To address the problems associated with single-objective path planning, herein, we propose a multi-objective path planning method that takes into account emissions, fuel consumption, and time as travel demands. First, a fuel consumption and emission estimation model, suitable for coupling with a dynamic transportation network, is established, and then, the multi-objective path planning model is presented. In addition, the analytic hierarchy process (AHP) and gray relational analysis are used to derive relative priorities of each sub-objective according to driver preference. The above two models are combined with an improved Dijkstra algorithm to perform multi-objective path planning based on dynamic time-dependent road conditions. Finally, the method is verified using real-world vehicle experiments on actual roads. Compared to single-objective path planning, the multi-objective planning method meets travel needs and can save time and fuel, thereby providing greater environmental protection while still considering driver preferences.