A traveler-centric mobility game: Efficiency and stability under rationality and prospect theory.

In this paper, we study a routing and travel-mode choice problem for mobility systems with a multimodal transportation network as a "mobility game" with coupled action sets. We formulate an atomic routing game to focus on the travelers' preferences and study the impact on the efficiency of the travelers' behavioral decision-making under rationality and prospect theory. To control the innate inefficiencies, we introduce a mobility "pricing mechanism," in which we model traffic congestion using linear cost functions while also considering the waiting times at different transport hubs. We show that the travelers' selfish actions lead to a pure-strategy Nash equilibrium. We then perform a Price of Anarchy and Price of Stability analysis to establish that the mobility system's inefficiencies remain relatively low and the social welfare at a NE remains close to the social optimum as the number of travelers increases. We deviate from the standard game-theoretic analysis of decision-making by extending our mobility game to capture the subjective behavior of travelers using prospect theory. Finally, we provide a detailed discussion of implementing our proposed mobility game.

A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions.

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial-temporal patterns of crime, and ambient population measures have a significant impact on crime rates.

Area-level associations of travel behaviour metrics with waist circumference: findings from linkage of travel and health surveys.

Individual-level analyses have consistently shown associations of travel behaviours with obesity-related measures. However, transport planning policies often target areas rather than individuals. To better inform transport-related policies and initiatives for obesity prevention, area-level relationships need to be investigated. This study linked data from two travel surveys with data from the Australian National Health Survey at the level of Population Health Areas (PHAs) and examined to what extent area-level travel behaviours metrics (prevalence of active travel, mixed travel and sedentary travel, diversity of travel modes) were associated with the rate of high waist circumference. Data from 51,987 travel survey participants were aggregated into 327 PHAs. Bayesian conditional autoregressive models were used to account for spatial autocorrelation. It was found that statistically replacing participants who relied on cars for travel (without walking/cycling) with those engaging in 30+ min/d of walking/cycling (without car use) was associated with a lower rate of high waist circumference. Areas with greater diversity of travel modes (mix of walking/cycling, car use, public transport use) also had lower prevalence of high waist circumference. This data-linkage study suggests that area-level transport planning strategies addressing car dependency, shifting car use to walking/cycling over 30 min/d, may help to reduce obesity.

A qualitative study of active travel amongst commuters and older adults living in market towns.

BACKGROUND: Being physically active is associated with better health, but rates of physical inactivity are high amongst adults in England. Active travel, defined as making routine journeys in physically active ways, has been identified as a potential solution. There is a lack of research into how modal shift towards active travel can be encouraged in market towns. The aims of the current study are to understand how new cycling and walking infrastructure and community activation projects might support modal shift to active travel amongst commuters and older adults in market towns. METHODS: This was a qualitative study using focus groups, 'go-along' interviews, and in-depth interviews as the main methods of data collection. Thirty-three participants (12 commuters and 21 older adults) took part across six focus groups. Eight of these also completed a go-along interview (4 walking, 4 cycling). Data were analysed using the Framework method of thematic analysis. RESULTS: Market towns have existing advantages for active travel, being relatively compact with most routine destinations within easy reach. The barriers to active travel faced by older adults and commuters in market towns are similar to those in cities; poor infrastructure remains the key barrier. Poorly maintained paths are hazardous for older pedestrians, and low-or-no lighting and lack of well-connected, delineated cycle routes deter both commuters and older adults. One factor which does appear qualitatively different to cities is participants' perception that the social norms of cycling differ in market towns. CONCLUSIONS: Policies to promote active travel in market towns are most likely to be effective when they include measures targeted at both individual behaviour change and population level measures like large-scale infrastructure improvements. Initiatives to change the social norms around cycling may be required to increase active travel rates.

Associations of active commuting to school in childhood and physical activity in adulthood.

This study examined whether active commuting to school in childhood and adolescence predicted active commuting to work and overall physical activity (PA) in adulthood. Participants from the Young Finns Study (N = 2436) were aged 9-18 years in 1980 and followed up until 2018/2020. Their commuting modes to school were assessed with a self-reported questionnaire in 1980. Adulthood PA was assessed through self-reports regarding commuting modes to work (2001-2018), leisure-time physical activity (LTPA) (2001-2018), and objectively measured daily steps (2007-2018/2020). Associations between childhood commuting and adulthood PA were evaluated using regression analyses and multilevel models. Demographic, socioeconomic and environmental covariates were adjusted for in the analyses. Active commuting to school in childhood contributed favourably to LTPA in 2001 (B = .38, p < .001), in 2007 (B = .35, p < .001), and in 2018 (B = .28, p < .01). Active commuting in childhood was associated with higher number of daily aerobic steps (B = 299.00, p = .03) and daily aerobic steps during weekdays in 2011 (B = 312.15, p = .03). In 2018, active commuting associated favourably with daily aerobic steps (B = 370.42, p < .01), daily aerobic steps during weekdays (B = 347.65, p = .01), daily steps during weekends (B = 628.49, p = .02), and daily aerobic steps during weekends (B = 402.69, p = .03). Covariate adjustments attenuated the associations excluding the one between active commuting and LTPA in 2007 (B = .36, p = .01) and daily steps during weekends in 2018 (B = 782.25, p = .04). Active commuting to school in childhood might be one of the PA modes that contribute to PA in adulthood and is therefore encouraged to be promoted from an early age.

Traffic assignment in urban transportation network problem with emission constraints in China: a cooperative game theory.

Traffic assignment in urban transport planning is the process of allocating traffic flows in a network. Traditionally, traffic assignment can reduce travel time or travel costs. As the number of vehicles increases and congestion causes increased emissions, environmental issues in transportation are gaining more and more attention. The main objective of this study is to address the issue of traffic assignment in urban transport networks under an abatement rate constraint. A traffic assignment model based on cooperative game theory is proposed. The influence of vehicle emissions is incorporated into the model. The framework consists of two parts. First, the performance model predicts travel time based on the Wardrop traffic equilibrium principle, which reflects the system travel time. No travelers can experience a lower travel time by unilaterally changing their path. Second, the cooperative game model gives link importance ranking based on the Shapley value, which measures the average marginal utility contribution of links of the network to all possible link coalitions that include the link, and assigns traffic flow based on the average marginal utility contribution of a link with system vehicle emission reduction constraints. The proposed model shows that traffic assignment with emission reduction constraints allows more vehicles in the network with an emission reduction rate of 20% than traditional models.

Human and machine drivers: Sharing control, sharing responsibility.

Machines are empowered with ever-increasing agency and decision-making authority to augment or even replace humans in various settings, making responsibility attribution less straightforward when they cause harm. Focusing on their applications in transportation, we consider human judgments of responsibility for automated vehicle crashes through a cross-national survey (N = 1657) and design hypothetical crashes after the 2018 Uber automated vehicle crash reportedly caused by a distracted human driver and an inaccurate machine driver. We examine the association between automation level-the human and machine drivers have different levels of agency (i.e., the human as a supervisor, backup driver, and mere passenger, respectively)-and human responsibility through the lens of perceived human controllability. We show the negative association between automation level and human responsibility, partly mediated by perceived human controllability, regardless of the involved responsibility metric (rating and allocation), the nationality of the involved participant (China and South Korea), and crash severity (injury and fatality). When the human and machine drivers in a conditionally automated vehicle jointly cause a crash (e.g., the 2018 Uber crash), the human driver and car manufacturer are asked to share responsibility. Our findings imply that the driver-centric tort law needs to be control-centric. They offer insights for attributing human responsibility for crashes involving automated vehicles.

Intermunicipal travel networks of Mexico during the COVID-19 pandemic.

Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society's local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020-2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

Validating the Perceived Active School Travel Enablers and Barriers-Parent (PASTEB-P) Questionnaire to Support Intervention Programming and Research.

A child's ability to participate in active school travel (AST) is complicated by several factors. Of particular note are parental controls, which are informed by their perceptions of the local built and social environments, assessments of their child's skills, and convenience preferences, among other considerations. However, there is currently a lack of AST-specific scales that include validated parental perception measures related to such notable barriers and enablers, or those that tend to frame their AST decision-making processes. Framed within the social-ecological model of health behaviour, the aims of the present paper were thus threefold, specifically to (1) outline and test the construct validity of measures delineating parental perceptions of barriers and enablers to AST, (2) evaluate the reliability and consistency of the developed measures, and (3) connect these measures to develop broader constructs for use in the Perceived Active School Travel Enablers and Barriers-Parent (PASTEB-P) questionnaire. To achieve these aims, a mixed-methods approach featuring cognitive interviews and surveys, along with qualitative (thematic analysis) and quantitative (Cohen's Kappa, McDonald's Omega, and confirmatory factor analysis) analyses, was applied across two studies. The validation processes of the two studies resulted in the development of fifteen items comprising seven distinct constructs (barriers: AST Skills, Convenience, Road Safety, Social Safety, and Equipment Storage; enablers: Supportive Environment and Safe Environment) related to parental perceptions of AST. The developed PASTEB-P questionnaire can be used to inform and evaluate AST intervention programming and can be applied for AST research purposes.

Environmental correlates of adolescent active travel to school in Asia: An ecological study.

OBJECTIVES: This study aimed to examine the association between country-level environmental correlates and the prevalence of active school travel (AST) in Asia and country-level differences in AST by age and sex. METHODS: This ecological study involved 31 Asian countries. Dependent variables were AST prevalence, AST prevalence difference by age, and by sex. Independent variables were country-level environmental correlates extracted using publicly available datasets, classified into physical and social environments. Association estimates of each dependent variable and each of the independent variables were calculated using univariate linear regression. All variables were standardized to have a mean of 0 and a standard deviation of 1. RESULTS: Results showed that 1 standard deviation (SD) difference in urban population percentage, night-time light, secondary-school enrolment, and prevalence of adult insufficient physical activity were negatively associated with AST prevalence (SD difference: -0.44 (-0.78 to -0.09), -0.40 (-0.76 to -0.04), -0.39 (-0.74 to -0.04), and -0.40 (-0.76 to -0.03), respectively). A 1 SD difference in car per people was associated with a -0.46 (-0.84 to -0.09) difference of AST prevalence by age. A 1 SD difference in PM2.5 concentration and of prevalence of adult insufficient physical activity were associated with a difference of 0.38 (0.01-0.74) and 0.42 (0.03-0.80) difference of AST prevalence by sex. CONCLUSIONS: This study shows that Asian countries with a greater number of people living in urban areas, lower levels of overall adult physical activity and higher levels of night-time light have a lower prevalence of adolescent AST. Country-level physical and social environmental correlates explained some of the regional variance in AST. Future policy actions and interventions for the region need to be contextually sensitive to the environmental correlates that vary between countries.

Material stock analysis of urban road from nighttime light data based on a bottom-up approach.

In recent years, there has been an increasing focus on the dynamics of material stock, that is, the basis of material flow in the entire ecosystem. With the gradual improvement of the global road network encryption project, the uncontrolled extraction, processing, and transportation of raw materials impose serious resource concerns and environmental pressure. Quantifying material stocks enable governments to formulate scientific policies because socio-economic metabolism, including resource allocation, use, and waste recovery, can be systematically assessed. In this study, OpenStreetMap road network data were used to extract the urban road skeleton, and nighttime light images were divided by watershed to construct regression equations based on geographical location attributes. Resultantly, a generic road material stock estimation model was developed and applied to Kunming. We concluded that (1) the top three stocks are stone chips, macadam, and grit (total weight is 380 million tons), (2) the proportion of asphalt, mineral powder, lime, and fly ash is correspondingly similar, and (3) the unit area stock decreases as the road grade declines; therefore, the branch road has the lowest unit stock.

Efficiency of portable air purification on public buses: A pilot study.

High concentrations of fine particulate matter (PM2.5) have been frequently reported in public transit systems and can cause adverse health effect. The portable air purifier is an inexpensive solution that could potentially clean in-cabin PM2.5. This study aims to find the PM2.5 removal efficiency of portable air purifiers in a public transit bus. In various scenarios, after artificially preloading the in-cabin PM2.5 concentration to 400 µg/m3, the concentrations were measured every 10 s, with and without the intervention of air purifiers. In a test bus with a volume of approximately 62.5 m3, three portable air purifiers were capable of reducing the average concentration of PM2.5 by 42-74%, from 400 µg/m3, to levels below 15 µg/m3, the acceptable short-term exposure concentration recommended by WHO. When high concentrations of outdoor PM2.5 entered the bus, purifiers maintained a relatively low level of in-cabin PM2.5. Air purifiers were more effective in reducing in-cabin PM2.5 than traditional air filtration and ventilation methods (air conditioning system filtration and door opening) in public transit buses. The deployed air purifiers reduced the concentration of particulate matter inside the bus, which may reduce the health risk of PM2.5 exposure and the spreading of airborne infections in public transit, thus, implying the potential to enhance passengers' and drivers' health.

The health impact of freight transport-related air pollution on vulnerable population groups.

Every year, over 364,200 people in Europe die prematurely due to the effects of air pollution, in which the transportation sector plays an important role. In Brussels, freight transport generates €61,604 of air pollution health costs daily. Research has shown that dynamic spatiotemporal modeling of both emission sources and exposed people (using mobile phone data) renders more accurate impact results when analyzed in microenvironments. However, mobile data underrepresent population segments that are more sensitive to the effects of air pollution, such as toddlers, children and elderly individuals. This paper examined the link between vulnerable people aged 0-3, 3-18 and >65 years and freight transport-related air pollution concentrations in the Brussels-Capital Region (BCR). To this end, dynamic tailpipe emissions and their spatiotemporal dispersion were calculated using output from the Transport Agent-Based Model (TRABAM) on a daily basis. Population densities were calculated as a function of the residences' occupancy rate and school/class size and opening hours. The effects of exposure were then evaluated using age- and sex-differentiated exposure-response functions and monetized using local hospital cost factors. Data were compiled for 2021. A strong overlap between people's presence at the institutions' locations was noticed with a peak in (freight) transportation movements in the city. The results showed that €37,000 [€34,517.47-€40,047.13] of freight transport-related air pollution health costs were incurred daily by vulnerable population segments. While these vulnerable groups made up 25.34% of the total BCR population, they incurred 60% [56.03%-65.01%] of the engendered transportation air pollution costs. The results were then geographically analyzed to identify 465 traffic-related air pollution hotspots across the territory, which accounted for €36,000 [€33,677.85-€39,101.31] of total costs. The latter can be used in future studies to assess sector-specific freight transportation policies, which should take into consideration spatiotemporal population densities on the local level.

Design and Validity of a Smart Healthcare and Control System for Electric Bikes.

This paper presents the development of an electronic system that converts an electrically assisted bicycle into an intelligent health monitoring system, allowing people who are not athletic or who have a history of health issues to progressively start the physical activity by following a medical protocol (e.g., max heart rate and power output, training time). The developed system aims to monitor the health state of the rider, analyze data in real-time, and provide electric assistance, thus diminishing muscular exertion. Furthermore, such a system can recover the same physiological data used in medical centers and program it into the e-bike to track the patient's health. System validation is conducted by replicating a standard medical protocol used in physiotherapy centers and hospitals, typically conducted in indoor conditions. However, the presented work differentiates itself by implementing this protocol in outdoor environments, which is impossible with the equipment used in medical centers. The experimental results show that the developed electronic prototypes and the algorithm effectively monitored the subject's physiological condition. Moreover, when necessary, the system can change the training load and help the subject remain in their prescribed cardiac zone. This system allows whoever needs to follow a rehabilitation program to do so not only in their physician's office, but whenever they want, including while commuting.

The Omicron Lineages BA.1 and BA.2 (Betacoronavirus SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub.

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation.

BGCP-based traffic data imputation and accident detection applications for the national trunk highway.

Facing the currently large quantity of intelligent transportation data, missing ones is often inevitable. Some previous works have shown the advantages of tensor decomposition-based approaches in solving multi-dimensional data imputation problems. However, a research gap still exists in examining the effect of applying these methods on imputation performance and their application to accident detection. Thus, referring to a two-month spatiotemporal traffic speed dataset, collected on the national trunk highway in Shandong, China, this paper employs the Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) to impute missing speed data in different missing rates and missing scenarios. Moreover, the dataset is built while considering both the temporal and the road functions. Applying the generated results of data imputation in accident detection is also of the main targets of this work. Thus, while combining multiple sources of data, such as traffic operation status and weather, eXtreme Gradient Boosting (XGBoost) is deployed to build accident detection models. The generated results show that the BGCP model can produce accurate imputations even under temporally correlated data corruption. Added to that, it is also suggested that, when there are continuous periods of missing speed data (missing rate greater than 10%), pre-processing of data imputation is imperative to maintain the accuracy of accident detection. Thus, the objective of this work is to provide insights into traffic management and academics when performing spatiotemporal data imputation tasks.