Search Results (106)

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

Urban tourism has experienced significant growth driven by platforms such as Airbnb, yet the relationship between short-term rental (STR) location and guest mobility remains underexplored. In this study, a structured survey of STR guests in Madrid during 2024 was administered face-to-face through property managers and luggage-storage services to examine factors influencing public transport (PT) use. Responses on bus and metro usage were combined into a three-level ordinal variable and modeled using ordered logistic regression against tourist demographics, trip characteristics, and accommodation attributes, including geocoded location zones. The results indicate that first-time and international visitors are less likely to use PT at high levels, while tourists visiting more points of interest and those who rated PT importance highly when choosing accommodation are significantly more frequent users. Accommodation in the central almond or periphery correlates positively with higher PT use compared to the city center. Distances to transit stops were not significant predictors, reflecting overall network accessibility. These findings suggest that enhancing PT connectivity in peripheral areas could support the spatial dispersion of tourism benefits and improve sustainable mobility for STR guests. Full article

This article reports on the interrogation of fiber Bragg grating (FBG)-based sensors that are multiplexed in an asymmetric tree topology. At each stage in the topology, FBGs are connected at one output port of a 50:50 coupler with fibers of different lengths. This asymmetric structure allows the simultaneous interrogation of long-distance and parallel sensor networks to be realized. Time- and wavelength-division multiplexing techniques are used to multiplex the FBGs. Using the heterodyne detection technique, high-sensitivity detection of reflection signals that have been weakened by losses induced by a round-trip transmission through the couplers and long-distance propagation is performed. Quasi-distributed FBGs are interrogated simultaneously, over distances ranging from 15 m to 80 km. Full article

The generation of waste grows yearly. In a centralized approach, more trucks are dispatched to collect the growing demand, with a higher pressure on the road network and greenhouse gas emissions. In contrast, a decentralized approach creates a network of distributed facilities. This study analyzes the impact of a decentralized approach for recyclable waste sorting facilities. It models waste generation, collection, and location of recyclable waste sorting facilities. This approach is applied to a case study in Montreal for polyethylene terephthalate. The case study computes two performance indicators: costs and CO₂ emissions. Six scenarios were developed and compared to a baseline scenario. The results show that decentralization reduces greenhouse gas emissions by 20.3% and operation costs by 8.04%. However, investment costs for the new facilities remain an obstacle. These costs can represent up to 89.7% of the expenses in a decentralized context. Nonetheless, decentralization increases the flexibility of waste collection under growing demand, since the distance to collect one ton has reduced by 35.3% and the average truck load per trip has reduced by 12.8%. To apply the model to the real world, further improvements are required. They span technical, economic, and social acceptability constraints. Full article

Internet congestion control focuses on balancing effective network utilization with the avoidance of congestion. When bottleneck bandwidth and network buffer capacities are exceeded, congestion typically manifests as packet loss. Additionally, when packets remain in buffers for too long, a queueing delay occurs. Most existing congestion control algorithms aim to solve this as a constraint satisfaction problem, where constraints are defined by bandwidth or queueing delay limits. However, these approaches often emphasize finding feasible solutions over optimal ones, which often lead to under-utilization of available bandwidth. To address this limitation, this article leverages Little’s Law to derive a closed-form optimality equation for congestion control. This optimality equation serves as the foundation for developing a new algorithm, TCP QtColFair, designed to optimize the sending rate. TCP QtColFair is evaluated against two widely deployed congestion control algorithms: TCP CUBIC, which utilizes a cubic window growth function to enhance performance in high-bandwidth, long-distance networks and TCP BBR (Bottleneck Bandwidth and Round-trip propagation time), developed by Google to optimize data transmission by estimating the network’s bottleneck bandwidth and round-trip time. In terms of avoiding queueing delays and minimizing packet loss, TCP QtColFair outperforms TCP CUBIC and matches TCP BBR’s performance when network buffers are large. For effective network utilization, TCP QtColFair outperforms both TCP BBR and TCP CUBIC. TCP QtColFair achieves an effective utilization of approximately 96%, compared to just above 94% for TCP BBR and around 93% for TCP CUBIC. Full article

In the last few decades, the promotion of active transport has been a viable solution recommended by transportation researchers, urban planners, and policymakers to reduce traffic congestion and improve public health in cities. To encourage active transport, it is important for cities to provide safe and accessible infrastructure for pedestrians and cyclists, as well as incentives for individuals to choose active modes of transportation over private vehicles. In this research, we focused on the suburb of Point Cook, located within the City of Wyndham in Melbourne’s west, owing to its rising human population and private vehicle ownership. The primary aim of this research is to examine the barriers in the interconnectivity of active transport networks for pedestrians and cyclists and to determine the segments of the transportation network that are not accessible to Point Cook residents. Our methodology is enshrined in the use of Social Pinpoint, which is an online interactive survey platform, and ground surveys (face-to-face interviews). In our assessment of the suburb of Point Cook, we utilised the concept of 20-min neighbourhoods to evaluate the accessibility of many important places within an 800-metre walking distance from residents’ homes. Based on our online interactive survey findings, approximately one-third of the individuals engaged in regular walking, with a frequency ranging from once a day to once every two days. One-third of the participants engaged in walking trips once or twice a week, whereas the remaining two-thirds conducted walking trips less frequently than once a week. Almost 89% of the participants expressed varying levels of interest in increasing their walking frequency. The findings showed that improving pedestrian and cycling networks that are easily accessible, well-integrated, inclusive, and safe is a prerequisite for achieving active transport and create neighbourhoods in which everything is accessible within a 20-min walking distance. Full article

Carbon emissions from transportation account for an increasing proportion of total carbon emissions, and daily travel carbon emissions are an essential part of carbon emissions from transportation. Urban form influences the transportation network layout, so the degree of influence of urban transportation development on daily travel carbon emissions varies according to urban form. This paper uses panel data from 254 prefecture-level cities in China from 2006 to 2019 to explore the impact of urban transportation development on daily travel carbon emissions based on the moderating effect of urban form. The results show that urban transportation development plays a pivotal role in significantly reducing daily travel carbon emissions. The urban form further amplifies the impact of carbon emission reductions. Specifically, polycentric urban structures enable residents to meet their daily travel needs through short-distance trips, thereby alleviating traffic congestion. The impact of urban transportation development on daily travel carbon emission intensity exhibits heterogeneity. In both low-carbon pilot cities and large cities, urban transportation development markedly decreases daily travel carbon emission intensity. Additionally, it is observed that cities with lower economic development levels exhibit more pronounced effects in carbon emission reduction compared to their more economically developed counterparts. This paper provides empirical support for rational planning of urban transportation systems and low-carbon development of daily travel. Full article

Telerobotics involves the operation of robots from a distance, often using advanced communication technologies combining wireless and wired technologies and a variety of protocols. This application domain is crucial because it allows humans to interact with and control robotic systems safely and from a distance, often performing activities in hazardous or inaccessible environments. Thus, by enabling remote operations, telerobotics not only enhances safety but also expands the possibilities for medical and industrial applications. In some use cases, telerobotics bridges the gap between human skill and robotic precision, making the completion of complex tasks requiring high accuracy possible without being physically present. With the growing availability of high-speed networks around the world, especially with the advent of 5G cellular technologies, applications of telerobotics can now span a gamut of scenarios ranging from remote control in the same room to robotic control across the globe. However, there are a variety of factors that can impact the control precision of the robotic platform and user experience of the teleoperator. One such critical factor is latency, especially across large geographical areas or complex network topologies. Consequently, military telerobotics and remote operations, for example, rely on dedicated communications infrastructure for such tasks. However, this creates a barrier to entry for many other applications and domains, as the cost of dedicated infrastructure would be prohibitive. In this paper, we examine the network latency of robotic control over shared network resources in a variety of network settings, such as a local network, access-controlled networks through Wi-Fi and cellular, and a remote transatlantic connection between Finland and the United States. The aim of this study is to quantify and evaluate the constituent latency components that comprise the control feedback loop of this telerobotics experience—of a camera feed for an operator to observe the telerobotic platform’s environment in one direction and the control communications from the operator to the robot in the reverse direction. The results show stable average round-trip latency of 6.6 ms for local network connection, 58.4 ms when connecting over Wi-Fi, 115.4 ms when connecting through cellular, and 240.7 ms when connecting from Finland to the United States over a VPN access-controlled network. These findings provide a better understanding of the capabilities and performance limitations of conducting telerobotics activities over commodity networks, and lay the foundation of our future work to use these insights for optimizing the overall user experience and the responsiveness of this control loop. Full article

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a 6 km by 10 km grid size scenario in Dublin in Ireland so that the best CP distribution is obtained; (2) to use a feedforward neural network (FNNs) model to predict the best parameter weight combinations and the corresponding CPs. CP deployment solutions are classified as successful when an EV is located within 100 m of a CP at the end of a trip. We find that (1) integrating the GEECharge and EV Portacharge algorithms with FNNs optimises the distribution of CPs; (2) the normalised optimal weights for the population density, POIs, and most used road parameters determined by this approach result in approximately 109 CPs being allocated in Dublin; (3) resizing the grid from 6 km by 10 km to 10 km by 6 km and rotating it at an angle of $-{350}^{\circ }$ results in a 5.7% rise in the overall number of CPs in Dublin; (4) reducing the grid cell size from 1 km² to 500 m² reduces the mean distance between CPs and the EVs. This research is vital to city planners as we show that city planners can use readily available data to generate these parameters for urban planning decisions that result in EV CP networks, which have increased efficiency. This will promote EV usage in urban transportation, leading to greater sustainability. Full article

Wi-Fi round-trip time (RTT) ranging has proven successful in indoor localization. Here, it is shown to be useful outdoors as well—and more accurate than smartphone code-based GNSS when used near buildings with Wi-Fi access points (APs). A Bayesian grid with observation and transition models is used to update a probability distribution of the position of the user equipment (UE). The expected value (or the mode) of this probability distribution provides an estimate of the UE location. Localization of the UE using RTT ranging depends on knowing the locations of the Wi-Fi APs. Determining these positions from floor plans can be time-consuming, particularly when the APs may not be accessible (as is often the case in order to prevent unauthorized access to the network). An alternative is to invert the Bayesian grid method for locating the UE—which uses distance measurements from the UE to several APs with known position. In the inverted method we instead locate the AP using distance measurements from several known positions of the UE. In localization using RTT, at any given time, a decision has to be made as to which APs to range to, given that there is a cost associated with each “range probe” and that some APs may not respond. This can be problematic when the APs are not uniformly distributed. Without a suitable ranging strategy, one can enter a dead-end state where there is no response from any of the APs currently being ranged to. This is a particular concern when there are local clusters of APs that may “capture” the attention of the RTT app. To avoid this, a strategy is developed here that takes into account distance, signal strength, time since last “seen”, and the distribution of the directions to APs from the UE—plus a random contribution. We demonstrate the method in a situation where there are no line-of-sight (LOS) connections and where the APs are inaccessible. The localization accuracy achieved exceeds that of the smartphone code-based GNSS. Full article

The predictors of urban trip mode choice and one of its important components, public transit ridership, have still not been thoroughly investigated using case studies in Central Europe. Therefore, this study attempts to clarify the correlates of mode choices for commute travel and shopping, and entertainment travel to distant places, as well as the frequencies of public transit use of university students, using a wide range of explanatory variables covering individual, household, and socio-economic attributes as well as their perceptions, mobility, and the nearby built environment. The correlation hypothesis of these factors, especially the role of the street network, was tested by collecting the data from 1288 university students in Krakow and developing Binary Logistic and Ordinal Probit models. The results show that gender, age, car ownership, main daily activity, possession of a driving license, gross monthly income, duration of living in the current home, daily shopping area, sense of belonging to the neighborhood, quality of social/recreational facilities of the neighborhood, and commuting distance can predict commute and non-commute mode choices, while gender, daily activity, financial dependence from the family, entertainment place, quality of social/recreational facilities, residential self-selection, number of commute trips, time living in the current home, and street connectivity around home are significantly correlated with public transit use. Some of these findings are somewhat different from those regarding university students in Western Europe or other high-income countries. These results can be used for policy making to reduce students’ personal and household car use and increase sustainable modal share in Poland and similar neighboring countries. Full article

Decarbonising holiday travel is crucial for climate change mitigation: policy interventions need to encourage less frequent trips, closer destinations, and travelling on the ground. To increase effectiveness, interventions should fit with the specific ways holidays are perceived and performed in each context. We explore the holiday behaviour of people living in a medium-sized city in Southern Switzerland (Lugano, 70,000 inhabitants), with the aim of identifying key intervention strategies for a future “community challenge” encouraging the population to take low-carbon holidays. We combine a literature review with n = 15 qualitative, semi-structured interviews that allow us to understand the reasons for taking a holiday, the favourite destination and activity types, and the transport mode choices. As Switzerland is characterised by high cultural and linguistic diversity providing the feeling of being abroad even at a short distance from home, it could be a valuable holiday destination for Swiss people themselves. Located at the centre of Europe, it is also well-connected by train with many holiday destinations abroad. Gaps between pro-environmental attitudes and holiday behaviour suggest leveraging digital carbon trackers showing how carbon emissions compare between holiday and everyday life. Also, interventions could leverage social norms via social networks, local influencers, and travel agencies. Full article

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders. Full article

The increasing popularity of moped scooter-sharing as a direct and eco-friendly transportation option highlights the need to understand travel demand for effective urban planning and transportation management. This study explores the use of machine learning techniques to forecast travel demand for moped scooter-sharing services in Madrid, Spain, based on origin–destination trip data. A comprehensive dataset was utilized, encompassing sociodemographic characteristics, travel attraction centers, transportation network attributes, policy-related variables, and distance impedance. Two supervised machine learning models, linear regression and random forest, were employed to predict travel demand patterns. The results revealed the effectiveness of ensemble learning methods, particularly the random forest model, in accurately predicting travel demand and capturing complex feature relationships. The feature scores emphasize the importance of neighborhood characteristics such as tourist accommodations, public administration centers, regulated parking, and commercial centers, along with the critical role of trip distance. Users’ preference for short-distance trips within the city highlights the appeal of these services for urban mobility. The findings have implications for urban planning and transportation decision-making to better accommodate travel patterns, improve the overall transportation system, and inform policy recommendations to enhance intermodal connectivity and sustainable urban mobility. Full article