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.

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.

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.

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.

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.

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,

This study investigates the potential of modular vehicle concepts and consolidation to increasethe efficiency of urban freight and passenger transport. Modularity is achieved by connectingmultiple vehicles together to form a platoon. Consolidation is realized by integrating passengerand freight demand in the routing problem. Vehicles are specific for each demand type but canbe connected freely, allowing the transport of multiple demand types in the same platoon. Therouting problem formulation considers travel time costs, travel distance costs, fleet size costs,and unserved requests costs. The operations are modeled in a novel modular multi-purposepickup and delivery problem (MMP-PDP) which is solved using CPLEX and Adaptive LargeNeighborhood Search (ALNS). In an extensive scenario study, the potential of the modularvehicle type is explored for different spatial and temporal demand distributions. A parameterstudy on vehicle capacity, vehicle range and platoon cost saving is performed to assess theirinfluence on efficiency. The experiments indicate a cost saving of 48% due to modularity and anadditional 9% due to consolidation. The reduction mainly stems from reduced operating costsand reduced trip duration, while the same number of requests can be served in all cases. Emptyvehicle kilometers are reduced by more than 60% by consolidation and modularity. A large-scalecase study in Stockholm highlights the practical applicability of the modular transport system.The proposed model and optimization framework can be used by companies and policy makersto identify required fleet sizes, optimal vehicle routes and cost savings due to different typesof operation and vehicle technology

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. Our findings suggest that 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 travelled 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.

Electric Vehicles sales have grown at an exponential rate all over the world. However, this industry still faces many challenges with lack of charging infrastructure being the main problem. This study analyzes the feasibility of mobile electric vehicle charging robots being researched by industry and academia alike and proposes an intelligent control algorithm using deep reinforcement learning algorithms. The algorithm uses Deep Deterministic Policy Gradient based framework and uses an actor-critic and model-free algorithm on the deterministic policy gradient to operate over continuous action spaces. The charging solution is compared with existing conventional solutions using simulations. The results obtained from simulations show that a mobile autonomous charging station can provide many benefits. Apart from having a low upfront investment cost as compared to static chargers, a smart mobile charger also offers greater flexibility. The algorithm also performs better as compared to conventional algorithms like least laxity factor and can easily be adapted to recent trends like shared mobility and autonomous mobility to provide a better user experience.

To better understand the interactions between physical built environment conditions and one’s well-being, we created a passive data collector for travellers and made the first step towards an explanatory model based on psychophysiological relations. By measuring biometric information from select trial participants we showed how different controlled factors are affecting the heart rate of the participants. A regression model with the impact factors such as speed, location, time and activity (accelerometer data) reveals how the factors relate to each other and how they correlate with the recorded individual’s heart rates throughout the observed period. For examples, the results show that the increase in movement speed is not linearly correlated with the heart rate. One’s heart rate would increase significantly when the individual reaches brisk walking and running speed, but not before nor after. Early morning and early evening time slots were the time where the observed individuals have the highest heart rates, which may correlate to individuals’ commute activities. Heart rates at the office would be lower than at home, which might correlate to more physical activities in the household.