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As a clean and renewable resource, solar energy is increasingly being used to relieve the pressures on environmental protection and the exhaustion of conventional energy. Although photovoltaic modules have been installed in many cities, the lack of quantitative mapping of the annual solar energy potential of urban surfaces hinders the effective utilization of solar energy. Herein, we provide a solar irradiation estimation solution for three-dimensional (3D) cities to quantify annual irradiations on urban envelopes and to investigate the effect of urban morphology on the resulting solar capacity. By modelling urban surfaces as 3D point clouds, annual irradiations of the point clouds were estimated. An empirical investigation across ten cities suggests that urban areas at lower latitudes tend to have larger values of annual irradiation; moreover, an area having greater building heights consistently has the largest third quartile of irradiation compared with lower buildings in the same city. Conversely, areas with many low buildings have a larger proportion of useable areas; in this arrangement, facades can optimally utilize solar energy, meaning that large irradiations are concentrated on certain facades. The Pearson correlation coefficients between solar capacity and urban morphology indices suggest that urban morphology has an important effect on solar capacity.

This study analyzes the influence of pedestrianization of urban space on the revenues of surrounding retail stores. Pedestrianization refers to the conversion of street use from vehicles to a walkable environment. We compiled a unique transaction dataset containing the estimates of sales volumes for stores across Spain and combine it with data from Open Street Map to provide the history of land-use changes at the street-level. Based on these high-granular datasets, we apply a difference-in-differences empirical method to measure the economic impact of pedestrian intervention. The results show that stores located in pedestrian environments tend to record higher sales volumes than stores located in non-pedestrian environments. We further analyze the mechanisms underlying this revenue-boosting effect and find that a key factor is the store density of the pedestrianized place, while geographic location is insignificant. This finding suggests that there are no differentiation impacts on stores' revenue based on whether pedestrianization occurs in the city center or periphery. Store category also acts as an important moderator for revenue impacts, with positive effects observed mostly for the cafe or restaurant category. Our results provide suggestive evidence that people prefer a pedestrian-friendly environment to a vehicle-oriented one for non-tradable, local consumption activities. This research provides evidence-based policy implications for urban planners interested in making smart, sustainable cities.

Knowledge on the dynamic properties of bridges in a city can improve condition assessments, maintenance scheduling, and emergency planning to better serve the public. Currently, bridge vibration data is obtained primarily by researchers through the use of a sophisticated sensor network that is composed of fixed sensor nodes. Recent studies have supported the alternative of mobile sensor networks, which are capable of delivering important structural information, e.g., modal properties, requiring less setup efforts and using fewer sensors. Simultaneously, digital technology has spawned data initiatives such as crowdsensing, in which individuals can collectively sense the urban environment. The prevalence of smartphones, which contain various advanced sensors, is rapidly restructuring researchers’ perceptions of data collection. This paper discusses the confluence of these emerging technologies, which can provide regular infrastructure data streams, within structural health monitoring (SHM) procedures for the immediate goal of system identification (SID) and towards automated maintenance of bridges. Will researchers continue to install sensor networks and collect their own data or will they start to source resident smartphone data? One of the objectives of this ongoing work is to quantify expected smartphone data stream volumes that would be applicable to SHM processes. As an example, the number of smartphones that traverse the Harvard bridge in a month is quantified.

Periodic exchange of situational information (beacons) is at the basis of most active safety applications in vehicular environments, and understanding its performance in real-world situations is very important. However, existing studies are focused on measuring the packet delivery rate (PDR), while disregarding the packet inter-reception (PIR) time which has recently been shown to be more relevant than PDR to characterize active safety application performance. In this paper, we fill this gap by presenting an extensive study of the PIR times observed in real-world highway scenarios.We start by showing that PIR cannot be reliably estimated from PDR, since the two metrics are only weakly correlated. Motivated by this finding, we present a thorough characterization of the PIR time distribution, which is shown to be a power law in a variety of configurations. The shape of the PIR time distribution indicates that potentially dangerous "situational awareness" blackouts are relatively frequent and positively time correlated. We then evaluate the effect of vehicle configuration and line-of-sight conditions on the PIR time, and show that relatively simple multi-hop beaconing techniques can substantially improve PIR statistics and, hence, safety. A final contribution of this paper is promoting the Gilbert-Elliot model, previously proposed to model bit-error bursts in packet switched networks, as a very accurate model of beacon reception behavior observed in real-world scenarios.

We study a new random search process: the taxi drive. The motivation for this process comes from urban sensing in which sensors are mounted on moving vehicles such as taxis, allowing urban environments to be opportunistically monitored. Inspired by the movements of real taxis, the taxi drive is composed of both random and regular parts: passengers are brought to randomly chosen locations via deterministic (i.e. shortest paths) routes. We show through a numerical study that this hybrid motion endows the taxi drive with advantageous spreading properties. In particular, on certain graph topologies it offers reduced cover times compared to random walks and persistent random walks.

Ridesharing has potential to mitigate traffic emissions. To better support policymaking, this paper endeavors to estimate and analyze emission reductions by large-scale ridesharing combining the Shareability-Network approach, the COPERT III emission model, and a speed-density traffic-flow model. Using Shanghai as a case, we show that ridesharing per se can reduce fuel-consumption (FC) by 22.88% and 15.09% in optimal and realistic scenarios, respectively, with corresponding emissions reductions. Ridesharing’s spontaneous first-order speed effect further reduces FC by 0.34–0.96%. Additionally, spatial analyses show that ridesharing reduces more emissions on severely polluted roads, leading to two spatial patterns; temporal analyses demonstrate patterns shifted from disorganized to organized. Both the phenomena can be explained by the aggregation of trips and the grading and topology of the roads. Moreover, ridesharing may also increase emissions on some branch roads, creating a new environmental injustice, which, however, is estimated to be less significant than expected.

The revolution in mobility-sharing services brings disruptive changes to the transportation landscape around the globe. The authorities often rush to regulate the services without a good knowledge of these new options. In Singapore and some other cities, dockless bike-sharing systems rose and fell in just one year and were followed by the booming of docking scooter-sharing systems. This study conducts a comparative analysis of bike-sharing and scooter-sharing activities in Singapore to help understand the phenomenon and inform policy-making. Based on the collected data (i.e., origin-destination pairs enriched with the departure and arrival time and the GPS locations) for one month, this study proposed methods to construct the paths and estimated repositioning trips and the fleet sizes. Hence, the spatio-temporal heterogeneity of the two systems in two discrete urban areas was investigated. It explored the impact of the fleet size, operational regulations (dockless versus docking), and weather conditions on the usages. We found that shared scooters have spatially compact and quantitatively denser distribution compared with shared bikes, and their high demands associate with places such as attractions, metros, and the dormitory. Results suggest that scooter sharing has a better performance than bike sharing in terms of the increased sharing frequency and decreased fleet size; however, the shareability still has potential to be improved. High repositioning rates of shared-scooters indicates high maintenance cost for rebalancing and charging. Rainfall and high temperatures at noon suppress the usages but not conclusively. The study also proposes several initiatives to promote the sustainable development of scooter-sharing services.

Solar accessibility, defined as the solar irradiation received in a spatial and temporal domain, is increasingly becoming a practical demand in a variety of applications, especially in dense and high-rise urban areas where people prefer natural daylighting accommodations and offices and enterprises desire rooftops with high exposure to sun for photovoltaic cells. However, new buildings may substantial alter spatio-temporal solar distribution and obstruct exposure to solar power significantly. Thus, providing an accurate quantification of how solar accessibility is impacted by a developing urban environment is a key step in the development of sustainable cities. Motivated by this observation, a solar irradiation estimation model has been designed, which allows solar radiation from a particular elevation and azimuth to pass through urban surfaces modelled as 3D polygons, resulting in the creation of 3D shadow surfaces. As such, the urban surfaces can also be represented as 3D point clouds of irradiations determined by both solar radiation and shadow. By applying the model to existing and planned urban environment, it is possible to estimate the transformation of solar accessibility at the district scale. As a case study, a master plan for the Kowloon East district proposed by the Hong Kong government has been considered, and an application of the propose methodology found that new buildings to be built in the district can obtain considerable solar energy, while having a marginal impact on existing buildings. This case study suggests that the model can be used in many other cities for a variety proposes.

During the past 100 years, many large cities around the world prioritized individual transportation in cars over more sustainable and healthier modes of transportation. As a result, traffic jams, air pollution, and fatal accidents are a daily reality in most metropolis, in both developed and developing countries. On the other hand, walking and bicycling are effective means of transportation for short to medium distances that offer advantages to both the city environment and the health of its citizens. While there is a large body of research in modeling and analysis of urban mobility based on motorized vehicles, there is much less research focusing on non-motorized vehicles, and almost no research on comparing pedestrian and cyclist behavior. In this paper, we present a detailed quantitative analysis of two datasets, for the same period and location, covering pedestrian and bike sharing mobility. We contrast the mobility patterns in the two modes and discuss their implications. We show how pedestrian and bike mobility are affected by temperature, precipitation and time of day. We also analyze the spatial distribution of non-motorized trips in Greater Boston and characterize the associated network of mobility flows with respect to multiple metrics. This work contributes to a better understanding of the characteristics of non-motorized urban mobility with respect to distance, duration, time of day, spatial distribution, as well as sensitivity to the weather.