Recent advancements in computer graphics and human-computer interaction have made it possible to create traffic simulators that contain complex environments for the participants to navigate. These can be used to understand how changes to the environment affect travellers without having to perform the changes in real life. There have also been improvements in how to collect information about what the user is experiencing in these simulators. There is a wide range of variables that could be used to better understand what the user is focusing on, but which of these are giving the most information? This paper aims to compare and uncover what the movements of the head and the eyes can explain regarding where a person is focusing their attention when searching for information. Using a VR HMD with built-in eye-tracking, participants were asked to search for objects in static and dynamic environments. The results showed that eye-tracking had a higher hit rate than the head-tracking, as expected. Information from the head-tracking was found in the eye-tracking to a higher degree than the eye-tracking was found in the head-tracking across all scenarios. In conclusion, eye-tracking shows signs that it is the preferred method for understanding information searching among travellers.

Although much effort has been put into assessing and improving OpenStreetMap (OSM) data quality, further research is required to determine its reliability and robustness for real-world applications. This study introduces a framework, built on open-source geospatial tools, for analysing the performance of OSM road data across different navigation applications. We tested this framework on an extensive 41,000 km road network in Australia. While our findings generally supported the quality of the OSM dataset, analyses of census data revealed two key relationships: first, a significant link between a city's population and the quality of its OSM data, and second, a strong influence of Information and Communications Technology (ICT) infrastructure on OSM data development. Furthermore, while navigation tests showed that OSM road networks performed reasonably well, scenario analyses highlighted several issues: a strong correlation between data quality and navigation accuracy; a negative impact of distance on OSM-based route accuracy for long inner- and inter-city routes due to accumulated errors; and the tendency of OSM to suggest sub-optimal paths for routes to isolated locations. This framework offers valuable benefits to a wide range of users. The OSM community can use it to assess data quality before application; individuals and businesses can easily evaluate its utility for navigation and route-planning; and local governments can benefit from improved quality control, particularly for projects involving Connected and Automated Vehicles (CAVs). Finally, the framework's capacity to design and analyse various routing scenarios provides new insights into overall road network quality.

Spread prediction models are vital tools to help health authorities and governments fight against infectious diseases such as COVID-19. The availability of historical daily COVID-19 cases, in conjunction with other datasets such as temperature and humidity (which are believed to play a key role in the spread of the disease), has opened a window for researchers to investigate the potential of different techniques to model and thereby expand our understanding of the factors (e.g., interaction or exposure resulting from mobility) that govern the underlying dynamics of the spread. Traditionally, infectious diseases are modeled using compartmental models such as the SIR model. However, this model shortcoming is that it does not account for mobility, and the resulting mixing or interactions, which we conjecture are a key factor in the dynamics of the spread. Statistical analysis and deep learning-based approaches such as autoregressive integrated moving average (ARIMA), gated recurrent units, variational autoencoder, long short-term memory (LSTM), convolution LSTM, stacked LSTM, and bidirectional LSTM have been tested with COVID-19 historical data to predict the disease spread mainly in medium- and high-income countries with good COVID-19 testing capabilities. However, few studies have focused on low-income countries with low access to COVID-19 testing and, hence, highly biased historical datasets. In addition to this, the arguable best model (BiLSTM) has not been tested with an arguably good set of features (people mobility data, temperature, and relative humidity). Therefore, in thisstudy, the multi-layer BiLSTM model is tested with mobility trend data from Google, temperature, and relative humidity to predict daily COVID-19 cases in low-income countries. The performance of the proposed multi-layer BiLSTM is evaluated by comparing its RMSE with the one from multi-layer LSTM (with the same settings as BiLSTM) in four developing countries namely Mozambique, Rwanda, Nepal, and Myanmar. The proposed multi-layer BiLSTM outperformed the multilayer LSTM in all four countries. The proposed multi-layer BiLSTM was also evaluated by comparing its root mean-squared error (RMSE) with multi-layer LSTM models, ARIMA- and stacked LSTM-based models in eight countries, namely Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Finally, the proposed multi-layer BiLSTM model was evaluated at the city level by comparing its average relative error with the other four models, namely the LSTM-based model considering multi-layer architecture, Google Cloud Forecasting, the LSTM-based model with mobility data only, and the LSTM-based model with mobility, temperature, and relative humidity data for 7 periods (of 28 days each) in six highly populated regions in Japan, namely Tokyo, Aichi, Osaka, Hyogo, Kyoto, and Fukuoka. The proposed multi-layer BiLSTM model outperformed the multi-layer LSTM model and other previous models by up to 1.6 and 0.6 times in terms of RMSE and ARE, respectively.Therefore, the proposed model enables more accurate forecasting of COVID-19 cases and can support governments and health authorities in their decisions, mainly in developing countries with limited resources.

Understanding people's face-to-face interactions is crucial for effective infectious disease management. Traditional contact tracing, often relying on interviews or smartphone applications, faces limitations such as incomplete recall, low adoption rates, and privacy concerns. This study proposes utilizing anonymized Call Detail Records (CDRs) as a substitute for in-person meetings. We assume that when two individuals engage in a phone call connected to the same cell tower, they are likely to meet shortly thereafter. Testing this assumption, we evaluated two hypotheses. The first hypothesis-that such co-located interactions occur in a workplace setting-achieved 83% agreement, which is considered a strong indication of reliability. The second hypothesis-that calls made during these co-location events are shorter than usual-achieved 86% agreement, suggesting an almost perfect reliability level. These results demonstrate that CDR-based co-location events can serve as a reliable substitute for in-person interactions and thus hold significant potential for enhancing contact tracing and supporting public health efforts.

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments so that they can quickly roll out infrastructure that enables large-scale EV adoption in all segments and accelerates the green transition of the transport sector. To this extent we propose a transport energy demand centric dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In it, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, BEV enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and interactively and continuously adjust and reoptimize network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access the grid capacity and real-estate and presence of competition) are gradually discovered/observed. The paper describes components and functional support of the system that is available as of a web based platform to support the planning of public fast charging networks for freight and long-distance private car trips in 26 European countries (https://thegordian.io/) and has been used in commercial pilots in both competitive and collaborative settings.

Recent advances in the development of modular transport vehicles allow deploying multi-purpose vehicles, which enable alternate transport of different demand types. In this study, we propose a novel variant of the pickup and delivery problem, the multi-purpose pickup and delivery problem, where multi-purpose vehicles are assigned to serve a multi-commodity flow. We solve a series of use case scenarios using an exact optimization algorithm and an adaptive large neighborhood search algorithm. We compare the performance of a multi-purpose vehicle fleet to a mixed fleet of single-purpose vehicles. Depending on cost parameters, our findings suggest that in certain scenarios, the total costs can be reduced by an average of 13% when multi-purpose vehicles are deployed, while at the same time reducing total vehicle trip duration and total distance traveled by on average 33% and 16%, respectively. The required fleet size can be reduced by 35% on average when operating multi-purpose vehicles. The results can be used by practitioners and policymakers to determine if the combined service of passenger and freight demand flows with multi-purpose vehicles in a given system will yield benefits compared to existing transport operations.

The COVID-19 pandemic highlighted the critical role of human mobility in disease transmission. Understanding and anticipating spatio-temporal exposure risk is crucial for effective public health interventions. Although various mobility datasets have been explored, the potential of anonymized Call Detail Records (CDRs), combined with social network analysis, remains largely untapped for exposure risk estimation. This study investigates the use of social network analysis for estimating spatio-temporal exposure risk, using anonymized Call Detail Records (CDRs) from Mozambique to model human mobility patterns. The study focuses on three centrality measures: weighted in-degree centrality, improved in-degree centrality, and weighted PageRank. Daily origin–destination matrices constructed from CDRs are used to build directed-weighted networks representing human flow between provinces and districts. The exposure risk scores derived from these measures are compared against a risk score calculated from daily COVID-19 case data and the time-varying reproduction number. Results at the province level show that while all three centrality measures reveal similar trends in spatio-temporal exposure risk, weighted PageRank demonstrates the highest correlation with the COVID-19-based risk score. Poisson regression models, built to predict COVID-19 cases using the three centrality measures, further support the strong influence of exposure risk on the number of cases, with the weighted PageRank model showing the best predictive performance. The district-level analysis, focusing on the Greater Maputo area, used the distribution of Points of Interest (POIs) for validation. The results indicate that education, financial, and transport POIs correlate with exposure risk across all three centrality measures. In general, weighted PageRank consistently outperforms the other two measures in capturing exposure risk across most POI categories. The study concludes that CDRs, in conjunction with weighted PageRank, can effectively estimate spatio-temporal exposure risk, aiding decision-makers in implementing informed interventions to mitigate disease spread.

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

Urban environment and mobility are threatened by traffic congestion due to the growth in the number of vehicles and urbanization. To address this problem, we propose a deep reinforcement learning-based (DRL) urban network geofencing (UNG) strategy for traffic management to improve traffic operations and sustainability. The proposed solution creates a real-time geofence that consists of several sub-networks where dynamic speed limit policies are implemented. The road links in each sub-network share the same speed limit policy in a control cycle. An actor-critic framework is developed to learn the discrete speed limits of sub-networks in a continuous action space, and a reward function is developed based on the average speeds of vehicles on the network. A twin delayed deep deterministic policy gradient (TD3) method is introduced for calibrating the actor-critic networks and solving the overestimation bias problem arising with the function approximation. Based on the traffic simulation of a real-world local network in Shanghai, the performance of the geofencing methods is investigated in various scenarios characterized by different levels of traffic demand and control settings. The findings suggest that the proposed TD3-UNG controller is capable of generating beneficial dynamic speed limit policies to reduce total time spent, emissions, and fuel consumption in various scenarios.

Unlike car navigation, where almost all vehicles can traverse every route, one route might not be optimal or even suitable for all pedestrians. Route geometry information, including tortuosity, twists and turns along roads, junctions, and road slopes, among others, matters a great deal for specific types of pedestrians, particularly those with limited mobility, such as wheelchair users and older adults. Offering practical routing services to these users requires that pedestrian navigation systems provide further information on route geometry. Therefore, this article proposes a novel method for extracting and analyzing the geometry properties of the shortest pedestrian paths, with a focus on open geospatial data across four aspects: (a) similarity, (b) route curviness,