Future Transp., Volume 3, Issue 1 (March 2023) – 23 articles

GIS models are currently available for a broad range of applications in mobility planning. However, it is not known how widespread the current use of GIS models is among European urban mobility planners, nor what their user experiences and needs are. There is therefore a risk that the development of GIS models for urban mobility planning will be mainly driven by technical possibilities and data availability rather than by the needs of the prospective users. To inform model developers and ensure a good match between model options and user needs, we conducted a survey investigating the current application of GIS models in urban mobility planning practice in Europe as well as model data availability and the needs and priorities of European mobility planners regarding GIS models. We received 51 valid responses from the transport departments of 42 cities from 21 European countries. For developers of GIS-based traffic models, the findings indicate that in Europe there is scope for wider adoption and further improvement. The models currently used are considered useful to support urban mobility planning, but more than 60% of the surveyed cities do not yet use them. Increased user-friendliness, in particular for non-experts, appears important to promote wider adoption. Availability of non-traditional types of data, such as real-time data or data at neighborhood level, is still limited in most cities, but this may rapidly change. Finally, there is also considerable interest in traffic models that integrate social and environmental aspects. Full article

Smart mobility is crucial for future smart cities. Traffic simulation software (TSS) is an important tool for efficient planning and management of road networks to achieve this goal. Many TSS tools have been developed for both microscopic and macroscopic homogenous traffic flow. However, only two (SUMO and HetroSim) are applicable to heterogeneous traffic. In this paper, HetroTraffSim is proposed to simulate macroscopic heterogeneous traffic flows at road bottlenecks. It is developed using the Unity3D engine and is based on a second-order traffic flow model. It is evaluated for a 360 m road segment on University Road, Peshawar, Pakistan. This segment contains a 78.5 m bottleneck which causes traffic congestion. This bottleneck is due to the construction of a pedestrian overhead bridge which reduces the three-lane road to two lanes. HetroTraffSim provides normalized traffic velocity, average traffic density, traffic flow, and time, as well as the temporal and spatial evolution of traffic. The results obtained show that a change in the distance headway affects the traffic flow, velocity, and density. Further, HetroTraffSim can be extended to automated traffic flows using raycasting. It can easily be used to create realistic traffic scenarios and the computational complexity is low due to the small-degree polynomials employed. HetroTraffSim can be used by traffic planners to improve traffic flow and public safety. Full article

Turning movement (TM) data of vehicular traffic at intersections are a basic input for signal timing design. Existing methods of collecting TM data are time- and cost-intensive. Using connected vehicle (CV) data is an alternative method. Trajectories of vehicles through an intersection can be constructed using CV data. However, because of the low number of CVs in the traffic stream, it is imprecise to consider TM data from CVs as representative of the whole traffic flow. To address this issue, a Kalman filter (KF) for estimating TM rates at intersections based on CV data under low market penetration levels using commercially available connected vehicle data was developed in this study. This method is independent of intersection geometry or the presence of shared lanes. The algorithm was evaluated using data from an intersection in Salt Lake City, Utah. The manually collected TM counts at this intersection were compared with the raw CV data as well as the results obtained from the developed methodology. The comparison shows that while TM counts based on raw CV data show severe violations in accuracy, making them unreliable, the method developed in this research gives results that have much lower accuracy violations. Full article

With e-commerce demand rising, logistic operators are investing in alternative delivery methods such as drones. Because of their aerial reach, drones can provide much needed utility in the last mile by taking the load off of vehicles delivering parcels to customers on the road. Our goal is to assess the potential drones have in mitigating traffic congestion. To do so, we develop a mathematical model for a bi-modal delivery system composed of parcels carrying trucks and drones, combining it with an optimization problem that can be solved for the socially optimal routing and allocation policy efficiently. Within this formulation, we include multiple stakeholder perspectives by modeling the objective function in terms of both traffic congestion and parcel latency. This allows our model to quantify the impact of drones on reducing traffic congestion, and simultaneously finds the path routing that minimizes the given objective. To account for the effects of stopping trucks on road latency, we simulate roads shared between trucks and cars by utilizing SUMO. We then use quadratic optimization techniques to test our proposed framework on a variety of real-world transportation networks. Our findings highlight the trade-off between reducing traffic congestion and increasing parcel latency—while routing trucks along less time-efficient paths may alleviate traffic congestion, this disproportionately increases the parcel latency. This suggests the need for a balanced approach that considers both factors when solving for the routing policy. Full article

Natural disasters may require that the residents of the affected area be evacuated immediately using a potentially damaged infrastructure. In this paper, we developed a mesoscopic simulation modeling approach for modeling traffic flow over a large geographic area and involving many people and vehicles. This study proposed a novel model, namely, Colored Deterministic and Stochastic Petri Net (CDSPN), which can mesoscopically provide an individual vehicular traffic dynamic. Each vehicle has a unique identifier, speed, distance to go, assigned target, and a specific route. It also proposed a method to automatically construct a Petri net model that represents the evacuation of Guilford County (GC), North Carolina, from standard Geographic Information Systems (GIS) shapefiles. We showed that this model could successfully simulate the dynamics of hundreds of thousands of vehicles moving on the highway system towards pre-specified safe targets such as medical facilities, exit points, or designated shelters. The vehicles are assumed to obey traffic laws, and the model reflects the complexities of the actual highway systems. The developed software can be used to analyze in reasonable detail the evacuation process, such as identifying bottlenecks and estimating efficiency and the time needed. An explicit list of 18 assumptions is stated and discussed. The Petri net for GC evacuation is reasonably massive, consisting of 35,476 places and 43,540 transitions with 531,595 colored tokens, where each token represents a vehicle in GC. We simulate the evacuation, develop statistics, and evaluate patterns of evaluation. We found that the evacuation took about 8.7 h. Full article

Knowledge about the perception of stakeholders regarding the effectiveness of last-mile bicycle deliveries is essential to promote solutions at a local and national level. However, their use is an incipient solution in the Brazilian context. This paper aims to analyse the potential of performing last-mile bicycle deliveries according to the perspective of Brazilian logistics operators. First, the variables that affect the decision of logistic operators to adopt bicycles were captured using the theory of planned behaviour. Later, the intention of behaviour was measured by estimating an ordered logistic regression. The results showed that environmental awareness, public policies and weather conditions negatively influence the intention of logistics operators to perform last-mile bicycle deliveries in Brazil. However, these variables are more cultural than operational, and could be changed by awareness of the importance of performing last-mile bicycle deliveries to promote sustainable cities. Additionally, the findings could encourage the development of sustainable freight policies from private companies to achieve sustainable future transportation. Full article

Crash hotspot identification (HSID) is an essential component of traffic management authorities’ efforts to improve safety and allocate limited resources. This paper presents a method for identifying hotspots using self-organizing maps (SOM). The SOM method was used to identify high-risk areas based on five commonly used HSID methods: crash frequency, equivalent property damage only, crash rate, empirical Bayes, and the societal risk-based method. Crashes on a major road in Iran were examined using the proposed method. Based on these criteria, high-risk locations were grouped into six clusters, which provided appropriate criteria for each location depending on the importance of the cluster. The findings show that the identification of crash hotspots tends to focus on areas with more crashes and deaths, demonstrating that the research methodology was appropriate. Full article

Carsharing, as an innovative mobility option, can potentially support the urban transition towards a more sustainable mobility system and achieving sustainable development goals. This short communication aims at providing the status of carsharing services in Italy, with a focus on Turin, before and after the COVID-19 pandemic. In this regard, an overview of the role of carsharing in urban traffic is provided. Then, carsharing services in Italy before and after the pandemic are mapped. Accordingly, the implications of the pandemic outbreak for carsharing services are discussed. The provided insights confirm that although carsharing services have been affected by the pandemic, even after returning to normal life, the usage of carsharing is still far below its pre-pandemic level in Italy. Finally, to support sustainable mobility and incentivize using shared mobility services, a research agenda for further research is proposed, targeting the research gaps regarding (i) the factors leading to lower usage of carsharing after the pandemic, (ii) potential solutions and policies to support carsharing services return to their pre-pandemic level and exceeding it, and (iii) examining the impact of provided supports by governments during the pandemic, such as bonuses for purchasing bikes and scooters, on the adoption of carsharing services for the post-pandemic era. Full article

Over the last two decades, mobile phone data have appeared to be a promising data source for mobility analysis. The structure, abundance, and accessibility of call detail records (CDRs) make them particularly suitable for such use. However, their exploitation is often limited to estimating origin–destination matrices of a restricted part of the population: regular travellers. Although these studies provide valuable information for policymakers, their scope remains limited to this subpopulation analysis. In the present work, we develop a collective mobility reconstruction method adapted to nonregular travellers. The method relies on the notion of the detour ratio, which makes it robust to the lack of mobile phone data as well as its application to large instances (large and dense telecommunication networks). It is used to conduct a longitudinal analysis of the macroscopic mobility patterns in Santiago de Cali, Colombia, thanks to call detail data shared by communication provider CLARO as part of a research project conducted by Citepa, Paris, the Green City Big Data Project. Full article

This research is an attempt to explore the nature and characteristics of urban travel behavior in the metropolitan area of the Iranian capital, Tehran. To this end, using the perspectives of 822 participants via a questionnaire survey, we assessed six major Travel Behavior Components (TBCs)—namely, travel mode, travel time, travel destination, travel frequency, travel choice, and travel purpose—from the lens of five different sociodemographic characteristics—that is, gender, age, family structure, and educational and occupational groups. Using SPSS-26, MAXQDA, and Structural Equation Model (SEM) via AMOS software, we analyzed priorities and preferences related to TBCs across different social groups and explored the impact of preferences of different social groups on TBCs in the Tehran metropolitan area. The results indicate that firstly, the tendency to travel by private transport has the highest share among all groups compared with other modes of travel. Secondly, we identified four major challenges affecting urban travel behavior in Tehran: lacking diverse options for urban traveling, old vehicles and infrastructure, traffic congestion, and unequal access to travel facilities in the city. Finally, we found that sociodemographic characteristics have a relatively strong and significant impact on TBCs. The study highlights the role of sociodemographic characteristics in travel behavior transformations and gives more insights into the travel behavior preferences of urban residents. Such insights would be effective for city policymakers and planners to enhance the quality of urban transportation. Full article

Advertising can influence and change consumers’ attitudes, habits, and behaviour and can therefore be a way to promote sustainable mobility and multimodality in urban transport systems. This paper focuses on the following questions: what is the impact of advertising on consumer behaviour in the context of sustainable mobility and multimodal decision making in urban transport systems? This paper assesses the impact of advertising on consumer behaviour in the context of the scientific literature, and discusses the methods and effectiveness of advertising in promoting sustainable mobility and multimodality in urban transport systems. Thus, this study examines the role of advertising and its impact on consumer behaviour, and provides a framework for the use of advertising to promote sustainable mobility and multimodality in urban transport systems. The study involved 396 inhabitants of major Lithuanian cities. Analytical descriptive, quantitative, and statistical research methods were applied. A quantitative research strategy was used to better understand the expression of advertising and its impact on the development of sustainable mobility and multimodality in urban transport. The results show that advertisements promoting sustainable mobility do not have a significant impact and only a small proportion of respondents associate advertisements with the development of sustainable mobility and multimodality in urban transport. This provides an opportunity to improve the information system of advertisements by exploiting the originality, text, content, and idea of the advertisement, which the study found to be one of the most influential factors in changing consumers’ attitudes and encouraging them to make travel decisions based on the principles of sustainable mobility and multimodality. In addition, television, the internet (including e-mail advertising), radio, and outdoor billboards can be among the most effective means of providing information in anticipation of the development of sustainable mobility and multimodality in urban transport. The results of the study will therefore have long-term value for the scientific debate on the impact of advertising on the promotion of sustainable mobility and multimodality in urban transport, and a practical relevance for providing guidelines for the development of sustainable mobility and multimodality in urban transport in the information advertising system. Full article

Nowadays, the unmanned aerial vehicle (UAV) has a wide application in transportation. For instance, by leveraging it, we are able to perform accurate and real-time vehicle speed detection in an IoT-based smart city. Although numerous vehicle speed estimation methods exist, most of them lack real-time detection in different situations and scenarios. To fill the gap, this paper introduces a novel low-altitude vehicle speed detector system using UAVs for remote sensing applications of smart cities, forging to increase traffic safety and security. To this aim, (1) we have found the best possible Raspberry PI’s field of view (FOV) camera in indoor and outdoor scenarios by changing its height and degree. Then, (2) Mobile Net-SSD deep learning model parameters have been embedded in the PI4B processor of a physical car at different speeds. Finally, we implemented it in a real environment at the JXUST university intersection by changing the height (0.7 to 3 m) and the camera angle on the UAV. Specifically, this paper proposed an intelligent speed control system without the presence of real police that has been implemented on the edge node with the configuration of a PI4B and an Intel Neural Computing 2, along with the PI camera, which is armed with a Mobile Net-SSD deep learning model for the smart detection of vehicles and their speeds. The main purpose of this article is to propose the use of drones as a tool to detect the speeds of vehicles, especially in areas where it is not easy to access or install a fixed camera, in the context of future smart city traffic management and control. The experimental results have proven the superior performance of the proposed low-altitude UAV system rather than current studies for detecting and estimating the vehicles’ speeds in highly dynamic situations and different speeds. As the results showed, our solution is highly effective on crowded roads, such as junctions near schools, hospitals, and with unsteady vehicles from the speed level point of view. Full article

Road-based logistics suffer from inefficiencies due to less-than-full load vehicle movements. Consolidating loads through shared-fleet collaborations (also known as freight pooling) can reduce such inefficiencies, and thereby reduce costs, vehicle-kilometres (vkm), and related emissions and congestion. Utilising a significant historical dataset of vehicle movements, the potential cost savings and environmental benefits of a shared-fleet operation involving collaboration between two public sector organisations, integrating both static (fixed-schedule) and dynamic (client-specific) demand within a healthcare setting, were quantified. A Sample Collection Service (SCS; transporting pathology samples from doctors’ surgeries to centralised laboratories for analysis) shared spare capacity in vehicles operated by a Patient Transport Service (PTS; transporting eligible non-emergency patients to/from routine hospital appointments) as an alternative to engaging an external courier company. Results suggested that a shared-fleet collaboration servicing 78 surgeries, alongside normal patient loads in an average of 24 PTS vehicles/day, produced reductions of 16%, 13% and 12% in costs, vkm and carbon dioxide emissions, respectively. Decision-makers within public sector organisations that operate own-account vehicle fleets could pursue policies that actively seek out opportunities to deploy shared-fleet solutions to improve vehicle utilisation and therefore reduce public sector spending and the detrimental effects of road logistics. Full article

The efficacy of the application of traffic models depends on a successful process of model calibration. Microsimulation models have a significant number of input parameters that can be optimized in the calibration process. This paper presents the optimization of input parameters that are difficult to measure or unmeasurable in real traffic conditions and includes parameters of the driver’s behavior and parameters of Wiedemann’s psychophysical car-following model. Using neural networks, models were generated for predicting travel time and queue parameters and were used in the model calibration procedure. This paper presents the results of a comparison of five different applications of neural networks in calibrating the microsimulation model. The VISSIM microsimulation traffic model was selected for calibration and field measurements were carried out on two roundabouts in a local urban transport network. The applicability of neural networks in the process of calibrating the microsimulation models was confirmed by comparison of the modelled and measured data of traffic indicators in real traffic conditions. Methods of calibration were validated with two sets of new measured data at the same intersection where the calibration of the model was carried out. The third validation was made at the intersection in a different location. The selection of the optimal calibration methodology is based on the model accuracy between the simulated and measured data of traveling time, as well as queue parameters. The microsimulation model provides access to the raw data of observed traffic parameters for each vehicle in the simulation. The dataset of the calibrated model simulation results of all travel times of the selected traffic flow was compared with the dataset of the measured field data to determine whether the data are statistically significantly different or not. Full article

Intelligent transportation systems (ITS) is a broad area that encompasses vehicle identification, classification, monitoring, surveillance, prediction, management, reduction of traffic jams, license plate recognition, etc. Machine learning has practical and significant applications in ITS. Intelligent transportation systems rely heavily on vehicle classification for traffic management and monitoring. This research uses convolutional neural networks to classify cars at fine-grained classifications (make and model). Numerous obstacles must be overcome in order to complete the task, the greatest of which are intra- and inter-class similarities between the manufacturer and model of vehicles, different lighting effects, the shape and size of the vehicle, shadows, camera view angle, background, vehicle speed, colour occlusion and environmental conditions. This paper studies various machine learning algorithms used for the fine-grained classification of vehicles and presents a comparative analysis in terms of accuracy and the size of the implemented deep convolutional neural network (DCNN). Specifically, four DCNN models, mobilenet-v2, inception-v3, vgg-19 and resnet-50, are evaluated with three datasets, BMW-10, Stanford Cars and PAKCars. The evaluation results show that mobileNet-v2 is the smallest model as it is not computationally intensive due to depthwise separable convolution. However, resnet-50 and vgg-19 outperform inception-v3 and mobilenet-v2 in terms of accuracy due to their complex structure. Full article

As an active mode of transportation, walking has proven to be an effective strategy to tackle environmental problems while improving health. The current paper highlights the main factors that either encourage or deter pedestrians in their daily trips by analyzing the arisen discussions in three different Focus Groups (FGs), which were later synthesized into factors ranking according to their positive or negative outcomes. The results help identify these factors and show how a variety of target groups (seniors, young adults, middle-aged adults, divided by male and female perspective) perceive the city in different ways: younger adults prioritize accessibility and quality of the infrastructure, females highlighted the insecurity suffered during nighttime trips, and older adults emphasized the relevance of street maintenance. Finally, the benefits of walking on physical and mental health are viewed positively amongst all target groups, considering walking as a means to help people reduce stress. Full article

At present, there are many methods to construct a vehicle driving cycle, such as the micro-trip-based method and the Markov chain method. Different methods have different advantages and disadvantages. To compare these methods, this paper uses the micro-trip-based method, the Markov chain method, and the method combining micro-trips and Markov chains to construct the representative driving cycle of a Xi’an urban bus based on the driving data of the Xi’an No.2 bus. Firstly, the driving data is collected and preprocessed. Then, representative driving cycles are constructed based on different methods. Finally, different driving cycles based on different methods are compared. By calculating and comparing characteristic parameters, velocity distributions, acceleration distributions, and vehicle-specific power distributions between different representative driving cycles, this paper shows the advantages and disadvantages of different construction methods. Full article

Bangladesh’s railway system mostly uses typical manual railway crossing techniques or boom gates through its 2955.53 km rail route all over the country. Accidents frequently happen at railway crossings due to the lack of quickly operating gate systems, and to fewer safety measures at the railway crossing as well. Currently, there are very few automatic railway crossing systems available (without obstacle detectors). Additionally, all of them are dependent on the national power grid, without a backup plan for any emergency cases. Bangladesh is still running a bit behind in generating enough power for its consumption; hence, it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents at railway crossing points. We use two infrared (IR) sensors to operate the railway crossing systems, which are controlled by an Arduino Uno. This newly designed level crossing system is run with the help of sustainable renewable energy, which is cost-effective and eco-friendly, and applied under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated the results of several renewable energy sources, including a hybrid system, and optimized the Levelized Cost of Energy (LCOE) and the payback periods. Full article

Although different European and national policies have set targets for a shift from fossil fuels to zero-emission heavy-duty trucks in the transport sector, their share is still rather low and mainly on a project level. The process of changing human perception in freight transport goes beyond the often discussed technical or economical aspects and demands research activities and perspectives from different stakeholders. Therefore, this paper delivers a stakeholder analysis together with a qualitative comprehensive analysis from different perspectives. It reveals the influence and importance as well as the general attitude of identified stakeholders concerning zero-emission heavy-duty trucks in logistics on a regional level in order to identify changes and barriers and to derive recommendations for action. The carried out socio-technical approach uses and adapts existing and approved scientific approaches with the general aim of creating a holistic assessment to accompany the development and implementation of zero-emission drive-systems in the transport sector. Full article

Trams are a meaningful means of public transport in urban traffic. However, trams have some well-known disadvantages. These include, for example, possibly long distances to the stop, long waiting times, and lack of privacy, among others. The innovative mobility concept “FLAIT-Train” offers solutions to the problems mentioned. The FLAIT-train operates on ordinary roads and is intended to offer DOOR-2-DOOR transport. In the first application phase, the FLAIT-train runs on exclusive lanes but in the future can mix with other traffic. They are designed as vehicles with 2 seats and 1 m width. The vehicle technology of FLAIT-trains is similar/identical to battery-electric autonomous vehicles. This paper uses traffic simulations to investigate whether FLAIT trains are a suitable alternative to conventional trams, taking simulated/theoretical transport capacities in passenger-kilometers per day into account. Using the software SUMO (“Simulation of Urban Mobility”), a realistic traffic scenario is generated. In this scenario, the operation of the FLAIT-Trains and the trams are simulated under the same conditions and based on statistical data. Based on the simulation results, the performances of the FLAIT-Trains and the trams are compared. Full article

Guangdong Province enjoys a very high economic status in China especially in terms of port construction. In response to the port development directions in China, the Guangdong government released a policy about the construction of Guangdong ports in the next 15 years. Based on the policy, this study proposes to evaluate the port efficiency of major ports in Guangdong Province during 2011–2020 using the Super-efficiency EBM-DEA model that considers undesirable outputs, and the spatial effect of port efficiency and its influencing factors is further analyzed using the spatial Durbin model. The empirical results shows that the overall port efficiency in Guangdong Province is not high and varies widely among port clusters, thereby lacking synergistic development. The results of the spatial Durbin model show that port efficiency is positively correlated with the level of economic development, port-city relationship and transportation structure, as well as negatively correlated with the efficiency of neighboring ports. The findings have a far-reaching impact on the development of port construction. Full article

Variable speed limits (VSL) aim at improving safety and traffic fluidity by increasing drivers’ awareness. In the present simulator study, VSL displayed on overhead gantries on a motorway were also displayed on a mobile phone, fixed on the vehicle’s centre console, with distance-based triggers (250 m vs. 500 m from the overhead gantry). Results showed drivers (N = 20) complied with the in-vehicle information, which was congruent with the upcoming gantry. The sooner the in-vehicle VSL, the faster the speed when speed limits increased. Similarly, the sooner the in-vehicle VSL, the slower the speed when speed limits decreased. Later in-vehicle VSL resulted in lower speed homogeneity, which is a safety concern. Speed homogeneity was greater when no in-vehicle VSL were displayed. Finally, the 70 mph VSL were affecting driving behaviour differently. These results suggested that there might be traffic disruption and more erratic longitudinal vehicle control on real roads. Full article