Mobility impacts of automated driving and shared mobility

Human mobility has been significantly affected by COVID-19 and associated travel restrictions imposed by government policies. This study examines changes in mobility patterns in Singapore during different stages of the pandemic using mobile phone data. Our results indicate that population mobility decreased over the COVID pandemic and is slowly increasing after the restrictions were lifted. However, there have been changes in the mobility patterns of the population. Despite the easing of COVID-19 measures, the population is making fewer trips and the trip distances are longer for some people. This change may be attributed to several factors. One of which is that the population has not quite come back to the pre-COVID working routine and has adopted a semi-work-from-home policy, where the staff is working in the office for a few days a week. The other factor is the habit of making more “purposeful” trips remains. Further, we investigate changes in mobility patterns among the classified explorers and returners. The results demonstrate that the change in mobility has a similar trend to the population as a whole. These observed changes in mobility patterns may become the new normal and should be taken into account for traffic management, business decision-making, policy-making, and urban or transport infrastructure planning.

3 - Trends in the Occupational Mobility of U.S. Men, 1962–1972

The present study examines the impact of lockdown measures taken to control the spread of COVID-19 on road fatalities. The study is based on the data collected over the duration of six months for fifteen countries based on severity of COVID-19. Lockdown stringency and six daily mobility measures were selected to study the change in road mobility patterns due to lockdown measures. The fifteen countries were categorized into two clusters (C1 and C2) and generalized linear mixed model was considered for analysis. The model results revealed that stringent lockdown measures and high residential mobility reduced Crash Fatality Ratio (CFR) by 0.36% and 1.84%, respectively in cluster C2. Further, in cluster C1, travel restrictions on workplace and recreational activities decreased CFR up to 0.31% and 0.26%, respectively. Thus, overall model results specified that stringent law enforcement and appropriate compliance from the road users can effectively lessen traffic-related fatalities in unprecedented times.

A nested logit model of mode choice for inland movement of export shipments: A case study of containerised export cargo from India

An investigation of commuting trip timing and mode choice in the Greater Toronto Area: Application of a joint discrete-continuous model

The transportation sector is considered one of the major contributors to greenhouse gas (GHG) emissions in metropolitan areas. Any efforts to reduce these emissions require strategic management of multiple transportation modes. This paper presents a method to identify opportunities to reduce GHG emissions by expanding commuter bus services and providing incentives to shift commuters from private cars to transit. The approach uses a nested multinomial logit model for mode choice in a region where modes include driving alone, carpooling, walking, cycling, and using four possible transit modes (ferry, commuter rail, rapid transit, and bus) by walk-access or drive-access. A model of existing conditions was calibrated with data from the Boston metropolitan area. Using an emission factor model based on average speeds from the California Air Resources Board, the net effect of a new commuter bus service on GHG emissions from transportation is estimated. Potential GHG reductions are weighed against the capital and operating costs of new transit services to quantify the cost-effectiveness of new commuter bus services for isolated origin-destination pairs. This modeling framework is used to optimize fares and bus frequency to identify the corridors with the most cost-effective potential for GHG reduction. Results are presented for the Boston region, demonstrating the feasibility of implementation and the potential magnitude of benefits for cost-effectively reducing GHG emissions associated with transportation. The method is general and can be applied in other cities around the world.

Discrete choice models and the housing price and travel to work elasticities of location demand

Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study

The Effect of Mobility Restrictions on the SARS-CoV-2 Diffusion During the First Wave: What Are the Impacts in Sweden, USA, France and Colombia

Reconstructing mobility from smartphone data: Empirical evidence of the effects of COVID-19 pandemic crisis on working and leisure

Mobility restrictions have been imposed by many countries in order to curb the spread the novel coronavirus disease. These vary in overall severity but also in the details of which kinds of activity and hence mobility has been permitted or restricted. This study uses the Oxford COVID-19 Government Response Tracker to measure the severity of restrictions on mobility in eight Western European countries but adds additional understanding on the nature of restrictions by combining this with mobility data from Google on different activities. The countries were classified into three categories based on the observed changes in mobility patterns, reflecting differences in the approach rather than severity. The paper then assesses the relationships between mobility patterns and the spread of the virus by looking at the growth rate ratio. The time lag for the highest correlation was observed to be in the range of 14–20 days in most cases. In some countries, however, there is no correlation between mobility in parks and spread of disease, suggesting this activity is relatively safe with appropriate social distancing. These findings support the use of social distancing measures in order to limit the spread of COVID-19 and could also be helpful in case of any future outbreaks of similar infectious diseases.

Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic. Most of the current studies utilize travel volume per day as the critical indicator and identify the impacted period by the dates of governmental lockdown or stay-at-home orders, which however may not accurately present the actual impacted dates. The objective of this study is to provide an alternative perspective to identify the normal and pandemic-influenced daily traffic patterns. Instead of only using traffic volumes per day or assuming the impacted travel pattern began with the stay-at-home order, the methodology in this study investigates the within-day time-dependent travel speed as time series, and then applies dynamic time warping algorithm and hierarchical clustering unsupervised classification methods to classify days into various groups without assuming a start date for any group. Using the state-wide travel speed data in Alabama, these study measures dissimilarities among within-day travel speed time series. By incorporating the dissimilarities/distance matrix, various agglomerative hierarchical clustering (AHC) methods (average, complete, Ward's) are tested to conduct proper unsupervised classification. The Ward's AHC classification results show that within-day travel speed pattern in Alabama shifted more than two weeks before the issuance of the State stay-at-home order. The results further show that a new travel speed pattern appears at the end of stay-at-home order, which is different from either the normal pattern before the pandemic or the initial pandemic-influenced pattern, which leads to a conclusion that a ‘new normal’ within-day travel pattern emerges. • An alternative perspective to identify the normal and pandemic-influenced daily traffic patterns. • The within-day time-dependent travel speed was investigated as time series. • Dynamic time warping and hierarchical clustering were used to classify days into various groups. • The Ward's AHC classification showed within-day travel speed pattern change in Alabama two weeks before the lockdown.

The continuously rising amount of traffic is becoming more and more of a problem that cities have to deal with it, as it causes congestion, noise and air pollution, among other side effects. As a solution approach for these diverse problems, many cities have already decided to install a toll, which is often designed as a congestion-pricing area. With that, they aim to reduce, if not completely prohibit, traffic in the inner cities, and to provide a source of additional income for the city budget. In order to get the maximum traffic benefits out of such a tolling area and to avoid traffic jams within the tolled area, it is necessary to control the system with an efficient algorithm. However, at the moment there are no dynamic, traffic responsive city tolling systems. The major reason is that such controlling mechanisms, especially if they have to represent a whole network, would require a lot of data. This data is not only difficult to collect and manage, particularly if most of the roads inside the network have to be measured, it also makes it hard to design and calibrate the control algorithm. With the help of a network fundamental diagram (NFD), representing the traffic state of the whole network, this data collection and management problem can be solved. This motivates the idea of using an NFD as a basis for controlling the pricing scheme of a city tolling system. To examine the feasibility of this approach, a microscopically simulated test network was created and a NFD for this network was developed. Using this NFD, a desired occupancy value for the whole network is set, and the toll height is calculated from different control functions of the occupancy inside the tolled network compared to the desired occupancy value. This toll height is subsequently included into the route choice algorithm of the microsimulation, shifting the traffic onto the non-tolled roads. In order to assess the different control approaches, evaluation criteria including a comparison of the resulting network occupancy with the desired one, the stability of the price, and the reaction speed are set. Several approaches to determine the prizing based on the fundamental diagram are developed and tested in the simulated network. The development of the traffic flow and network occupation over time inside the tolled network is compared between the various tolling functions in order to identify the best performing one.

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

This work consists of two parts. In the first one are presented some general characteristics and are analyzed vertical social mobility of power elite's in Serbia and Croatia. Special attention was paid to intergenerational and intergenerational mobility fluxes. The researches said there was changes in mobility patterns i.e. in structure of power elites at last three decades ago. Participation of manual professions was going down for the benefits of nonmanual ones. There was used log-linear analyze its hierarchical method because finding statistics importance of connection between presented factors of social mobility. Thanks to this method we can see details of complex relations of interdependence of this factors.