Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This study reviews the literature on the usage of smart parking sensors, technologies, applications and evaluates their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this study suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real-time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

Big Data is an emerging paradigm and has currently become a strong attractor of global interest, specially within the transportation industry. The combination of disruptive technologies and new concepts such as the Smart City upgrades the transport data life cycle. In this context, Big Data is considered as a new pledge for the transportation industry to effectively manage all data this sector required for providing safer, cleaner and more efficient transport means, as well as for users to personalize their transport experience. However, Big Data comes along with its own set of technological challenges, stemming from the multiple and heterogeneous transportation/mobility application scenarios. In this survey we analyze the latest research efforts revolving on Big Data for the transportation and mobility industry, its applications, baselines scenarios, fields and use case such as routing, planning, infrastructure monitoring, network design, among others. This analysis will be done strictly from the Big Data perspective, focusing on those contributions gravitating on techniques, tools and methods for modeling, processing, analyzing and visualizing transport and mobility Big Data. From the literature review a set of trends and challenges is extracted so as to provide researchers with an insightful outlook on the field of transport and mobility.

Identification of recurrent bottlenecks is an effective way to hone an appropriate investment in current facilities to relieve congestion. Furthermore, it would enable the ranking or prioritisation of bottlenecks since bottleneck removal and its associated impact alleviation are hampered by limited sources. It is imperative that transportation jurisdiction understand and identify the basis for ranking bottlenecks by exploring: how often they are active; how long it takes the congestion to disappear; and how many miles of road are affected. Previous bottleneck identification schemes have focused on identifying congestion with little attention to distinguishing the recurrent level at the same ‘bottleneck’ location. In contrast to traditional schemes, a data-driven approach for identifying recurrent bottlenecks is introduced, using probe vehicle speed reports. The historical spatiotemporal characteristics of bottlenecks are investigated through a comprehensive analysis of 2253 miles of all state-wide interstates in North Carolina. Using the characteristics determined the recurrent bottleneck locations with a historical time span of bottleneck activation are revealed and tested. The findings of the proposed identification schemes generate critical information in order to quantify and diagnose a bottleneck and its associated impact area.

This paper presents a classification approach for road-user modes of travel. The classification does not assume well organized, and lane disciplined traffic. Instead, it relies on specific characteristics intrinsic for each road-user to predict the corresponding class. The classification relies on extracting the geometric and movement characteristics of road-users. As such, it is possible to classify road-users in shared space facilities and sites with high level of non-compliance. The classification is a multi-step procedure. First, movement features are used to discriminate between motorized and non-motorized road-users. Then, complementary features based on road-user geometry are added to differentiate between vehicles, rickshaws, powered two-wheelers, and buses. Experiments are performed on a video data set from a shared facility in New Delhi, India. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 90 percent. By considering the movement attributes, the approach is tolerant to considerable variations in road-user physical details which often arises from choices of camera positions and partial occlusions. The research is part of the long-term goal to develop an automated video-based road safety and data collection system for developing countries.

Traditional algorithms, such as genetic algorithm and simulated annealing, have greatly attracted a lot of research studies due to their simplicity and flexibility to solve coloured travelling salesman problem (CTSP). However, their performance is limited in solution quality and convergence speed. To improve these limitations, this study presents a fast and effective ant colony optimisation (FEACO) algorithm. In the proposed FEACO, a new pheromone updating mechanism is incorporated into the traditional ant colony optimisation (ACO) to improve its performance. Furthermore, a multi-task cooperative learning approach is employed to solve CTSP. In it, multiple tasks in a shared city set are cooperatively carried out by all salesmen, but each task in exclusive city sets is independently performed by an appointed salesman. In ACO and FEACO, feasible solutions can be found for CTSP by cooperative learning, which is carried out by the cooperation of a set of ants. During the process, ants can cooperate to find good solutions by utilising the pheromone deposited on the paths while ants pass them. Compared with state-of-the-art algorithms, the experimental results on both small scale and larger scale show that the proposed method has potential to provide an improvement in solution quality and convergence speed.

In this study, an enhanced approach for automotive radar systems is proposed to solve the detection, tracking, and track management problem in the presence of clutter with high accuracy and low computational cost. The unscented Kalman filter (UKF) with a constant turn rate and acceleration (CTRA) dynamic model is employed for target tracking, and the tracking accuracy is enhanced by incorporating the linear regression (LR) algorithm into the UKF-CTRA algorithm. We investigate, for the first time, the Joint Probabilistic Data Association (JPDA) algorithm for data association, and the composite M/N tests for track management. The capability of the proposed approach (CTRA-UKF-LR-JPDA-composite-M/N-tests) is demonstrated by comparing it with various algorithms for different single and multi-target tracking scenarios and for various sets of parameter regimes. The results show the superior performance of the proposed method over other existing techniques in automotive radar systems. This reveals the effectiveness of the proposed algorithm as a promising technique in automotive applications.

The use of web services for analysing and visualising maps has received great attention recently, because the complicated analysis of spatial data requires different processes to be run consecutively. Predicting the future location of a vehicle on a street network is one of the most challenging analyses used for improving context-aware location-based services, intelligent transportation systems and criminology. In this research, the authors present a new short-term prediction algorithm and explore the required analyses and web services. They present an appropriate method for chaining these web services to predict location(s). To assess their methodology, they developed a prototype system and tested for trajectories in Beijing. This system calculates the prediction time for a specified car to show the predicted future location in the street network. Their results showed that the average transferred data volume increases as the prediction period increases. The results also showed that the prediction algorithm has 75% accuracy at 1 min and 87.5% accuracy at 2 and 3 min. The implemented chaining method reduces the complexity of the location prediction algorithm for users because they do not need to know the processes. The outputs from this system can be used as input parameters for other web-based applications.

A cross-regional study of driving behaviour and technological preferences in typical driving scenarios, especially dangerous pre-crash scenarios, is presented as a contribution to the user experience design of in-vehicle driver assistance functions. Data from 46 participants are collected by one-to-one interviews following viewing of 11 video clips previously obtained from naturalistic field operational tests and representative of typical real driving scenarios. Six questions relating to each driving scenario are asked to reveal the differences between Chinese drivers and Swedish drivers. The results show similarities and differences in driving risk perceptions, decisions, and preferences concerning the assistance and specifics of potential advanced driver assistance system (ADAS) functions of drivers in China and Sweden. The preferences for assistance and ADAS functions are found to be correlated with relative driving risk perceptions and decisions in typical driving scenarios for both country groups. Based on the results, some suggestions for the design of driver–vehicle interactions for Chinese drivers are presented.

A multi-objective model was developed to optimize departure intervals synchronously for multiple bus lines. Then, a Genetic Algorithm with “Elitist Preservation” strategy combining the economical method of “dynamic scoring” (GA-EPDS) was proposed to solve the multi-objective model. The proposed method included three objectives: the first objective was to maximize the bus operation profits; the second objective was to minimize the passengers’ transfer waiting time; and the last one was to minimize passengers’ costs. Transfer waiting time was crucial for multiple bus lines and long transfer waiting time would decrease the satisfaction of passengers, so transfer waiting time was regarded as a single objective. In addition, an evaluation function, which was obtained through a “dynamic scoring” method, was formulated to estimate whether the three objective functions reached a global optimum. In order to improve the solution generated in terms of computational effort and convergence, a GA-EPDS was designed to solve the multi-objective model. Finally, the proposed approach was applied in a case study of an actual network. The numerical results based on different scale instances and different traffic conditions demonstrate that our proposed model and method are effective and feasible to optimize departure intervals for multiple bus lines.

With the development of rail transit, driver vigilance is increasingly important in railway safety. A vigilance detection method based on high-speed rail (HSR) is presented in this study. The proposed method includes three main parts: (i) a wireless wearable electroencephalography (EEG) collection module; (ii) HSR driver's vigilance detection module; and (iii) an early warning module. Drivers’ vigilance is monitored using eight EEG channels. A low-rank matrix decomposition (also called robust principal component analysis) algorithm is used to classify EEG signals which are collected through wireless wearable EEG collection technology. The warning module will sound an alarm and the early warning begins to message the train control centre if the driver is judged as fatigue. The method was tested on driving EEG data from ten different drivers and reached 99.4% correct classification in a 9 s time window. The feasibility of the proposed vigilance-detecting method for HSR safety is demonstrated through simulation and test results.

Many analytical procedures, technical methods, and tools have been developed to facilitate manual inspection of traffic congestion and support the decision-making process for traffic authorities. However, lacking an automatic mechanism, it would be a time-consuming and labour-intensive process for day-to-day and location-by-location analyses. This study presents a method based on a three-stage framework that is capable of automatically identifying and characterising spatiotemporal congested areas (STCAs) by parsing, extracting, analysing and quantifying the knowledge contained in traffic heatmaps. The key components of the proposed method are two unsupervised clustering procedures: (i) a mini-batch k-means clustering algorithm to separate the congested and non-congested areas and (ii) a graph-theory-based clustering algorithm to distinguish between different STCAs. Twenty weekdays of dual loop detector data collected from a 26-mile stretch of Interstate 10 in Phoenix, Arizona was analysed for the case study. The new method identified and quantified 102 STCAs without the need for human intervention. Based on 14 traffic measures calculated for each STCA, 19 active bottlenecks along the study corridor were identified. Top-ranked bottlenecks identified in this study were consistent with those reported in previous studies but were produced with less effort, demonstrating the new method's potential utility for traffic congestion management systems.

Pedestrian accessibility to a large four-leg roundabout can be improved by locating staggered crosswalks on all roundabout legs and signalising them to allow for two-stage crossing. A multi-level pedestrian signalisation (MPS) is developed for such roundabouts to balance pedestrian accessibility and vehicle mobility. With appropriate application of traffic control devices and traffic detection systems, the right-of-way is assigned to pedestrians and vehicles in three actuated modes. Signals at adjacent crosswalks can operate independently or as a group in a specific mode of right-of-way to prioritise pedestrians or vehicles. A number of traffic events are defined to autonomously manage signal operations second by second. Simulation results indicated that MPS had good operational stability and offered sufficient capacity for a large four-leg roundabout to serve traffic demand varying in a wide range. The appealing operational advantage of the MPS-enabled roundabout over the fully actuated conventional intersection may help prevent large four-leg roundabouts from being converted to conventional intersections.

Forecasting freight traffic contributes to the improvement of traffic facilities and making industrial policy, so it is significant to predict freight volume accurately. Extensive works had proved that ensemble model performed better than single model, so an ensemble model, combining seasonal autoregressive integrated moving average (SARIMA) with deep belief network (DBN), is proposed here. SARIMA, a linear model, is used to find the regularities of railway freight traffic. DBN, a non-linear model, is taken to mine the complex relationships between indexes and railway freight. In order to decide appropriate architecture of DBN, including the number of network layers and neurons in each hidden layer, Gaussian particle swarm optimisation algorithm is designed to decide appropriate architecture of DBN, including the number of network layers and neurons in each hidden layer. Besides, Spearman rank correlation analysis is used for selecting indexes related to freight volume. Experimental results show that, compared with SARIMA, DBN, back propagation neural network, Elman neural network, and radial basis function neural network, the proposed ensemble model obtains best performance, and the mean absolute error is 5.5159 million t and the mean absolute percentage error is 1.9657%.

Eliminating mishaps due to human error is a primary focus of the avionics industry. Air-lane monitoring is critical to avert occurrences of such mishaps and is achieved using intelligence imparting techniques. Fuzzy logic is one such technique, which incorporates human knowledge for decision making with ease. Type-1 fuzzy logic for decision fusion is established to be advantageous for air-lane monitoring considering sensor input data. Decision making capabilities of type-1 systems are inconsistent when uncertainties or noise is present in the input data. To overcome this issue, this study discusses on interval type-2 fuzzy logic-based decision fusion software (IT2FLDS) for air-lane monitoring. IT2FLDS is realised using an interval type-2 Mamdani model. Experimental results presented prove that IT2FLDS exhibits better decision making capabilities when compared with type-1 fuzzy logic systems considering uncertainties in input sensor data. IT2FLDS is further extended to include flight level parameters for air-lane monitoring. Results presented prove that IT2FLDS works better than its type-1 counterpart when flight level examples are considered. Using type-2 fuzzy logic systems for avionics problems related to air-lane discipline is advocated.

It is expected that soon there will be a significant number of unmanned aerial vehicles (UAVs) operating side-by-side with manned civil aircraft in national airspace systems. To be able to integrate UAVs safely with civil traffic, a number of challenges must be overcome first. This study investigates situational awareness of UAVs’ autonomous taxiing in an aerodrome environment. The research work is based on a real outdoor experimental data collected at the Walney Island Airport, the UK. It aims to further develop and test UAVs’ autonomous taxiing in a challenging outdoor environment. To address various practical issues arising from the outdoor aerodrome such as camera vibration, taxiway feature extraction, and unknown obstacles, the authors develop an integrated approach that combines the Bayesian-network based semantic segmentation with a self-learning method to enhance situational awareness of UAVs. Detailed analysis of the outdoor experimental data shows that the integrated method developed in this study improves the robustness of situational awareness for autonomous taxiing.

Traffic has become a major problem in metropolitan areas across the world. It is critical to understand the complex interplay of a road network and its traffic states so that researchers and planners can improve the city planning and traffic logistics. The authors propose a novel framework to estimate urban traffic states using GPS traces. Their approach begins with an initial estimation of network travel times by solving a convex optimisation programme based on Wardrop equilibria. Then, they iteratively refine the estimated network travel times and vehicle traversed paths. Lastly, using the refined results as input, they perform a nested optimisation process to derive traffic states in areas without data coverage to obtain full traffic estimations. The evaluation and comparison of their approach over two state-of-the-art methods show up to 96% relative improvements. In order to study urban traffic, the authors have further conducted field tests in Beijing and San Francisco using real-world GIS data, which involve 128,701 nodes, 148,899 road segments, and over 26 million GPS traces.

This study designs a process for identifying trips and activities based on global positioning system (GPS) survey data. The proposed identification process is composed of four steps, namely determining status segments, detecting activities, identifying trips, and recognising short-time activities. The results indicate that the proposed algorithm shows a high level of identification accuracy compared with the travel diaries reported in the paper-form travel survey. By providing the identification method of the short-time activities, this study resolves the problem of overlooking short-time activities in conventional travel surveys and increases the accuracy of trip detection. This work also facilitates the study of the spatial and temporal distributions of short-time activities related to travel behaviours such as temporary parking. By proposing a method for identifying trips and activities from GPS data, the findings provide a research scheme for detecting other travel information based on GPS data such as travel mode and trip purpose, reasonable decisions for urban transportation planning and management.

Recently, signal control strategies with decentralised logic have been developed to tackle the traffic congestion problems of urban road networks. Such strategies aim at network-wide traffic flow efficiency improvement via local actions, thus low design effort and infrastructure investment. This study presents, compares, and evaluates two such innovative approaches: the job scheduling algorithm comprising the local control component of the scalable urban traffic control (SURTRAC) system and the max- or back-pressure algorithm. The approaches are also compared against traffic-responsive urban control (TUC), a well-established strategy with centralised logic. Evaluation is based on the AIMSUN simulation model of the city centre of Chania, Greece. The study results indicate that the TUC and max-pressure retain performance independently of the prevailing traffic conditions, while also being computationally simpler than job scheduling. Both decentralised approaches require frequent (high-resolution) and relatively accurate measurements; on the other hand, TUC, although less demanding in this respect, calls for communication lines between the junction controllers and the central computer. Finally, compared with both decentralised approaches, the TUC provides a signal plan sequence with less excessive differences between each other, thus fewer disturbances to the common network users. Nevertheless, for more comprehensive conclusions, more investigations, including field trials, would be needed.

Reliable continuing positioning is a critical requirement for intelligent transportation systems (ITS). An integrated positioning system is presented, where the global navigation satellite systems (GNSS) real-time kinematic (RTK) method was mainly used. When RTK is not available, positioning was maintained by using Doppler measurements or by low-cost inertial measurement unit (IMU) coupled with vehicle odometer measurements. A new integrity monitoring (IM) method is presented that addresses each positioning mode of the proposed integrated system. Models for the protection levels (PLs) are presented to bound the position error (PE) along the direction of motion of the vehicle and for the cross-track direction. Both direction components are needed, for instance for collision avoidance and for lane identification. The method was assessed through a kinematic test performed in a dense urban environment. Results showed that by integrating GNSS RTK, Doppler with IMU + odometer, positioning was available all the time. For RTK, positioning accuracy was less than a decimetre and the IM availability was 99%, where the PLs bounded the PEs and were less than an alert limit of 1 m. Positioning using Doppler and IMU + odometer measurements bridged RTK breaks but at the sub-meter level accuracy when used for short periods.

In this study, the authors develop a state transition probability model for an isolated intersection on the basis of link-based state transition probability matrix. The state of an intersection is defined as the combination of its input links’ states which represent their respective congestion level by occupancy rate. This way of modelling is further extended to a compact region by likening the region to a virtual node with several virtual input links to avoid dimension expansion. Based on the state transition probability model, the stochastic signal control problem for both intersections and compact regions is formulated as a Markov decision process with the specified definition of state, action, probability and reward. A sensitivity-based policy iteration algorithm is employed to solve the Markov decision process in real-time, which has a great advantage in computational efficiency. The results of the numerical study on a calibrated network of Caohejing District in Shanghai indicate that the authors’ proposed method outperforms the fixed-time and actuated signal control at high loads in terms of many indices and greatly decreases the variability in the traffic performance. In addition, the compact region control can improve the optimisation efficiency while providing similar performance to the intersection control at different loads.

To enhance safety and traffic efficiency, stability of a mixed human and connected cruise control (CCC) system is studied. The authors consider individual vehicle platoons, in which the tail CCC vehicle receives feedback from multiple human-driven vehicles ahead via vehicle-to-vehicle communications, with the objective of stability analysis and feedback control design. To deal with this, the transfer function theory is used. Simulations are also performed to evaluate impacts of the mixed human and CCC system on safety and traffic efficiency. Results show that the output bounds of the CCC feedback coefficients can be appropriately designed to keep local individual vehicle platoons stable for all possible vehicle speeds. The feedback coefficient has a larger design range and the required lower bound value decreases as the feedback length increases. Additionally, the system design would improve traffic safety and efficiency even at lower CCC vehicle penetration rates.

Train-centric communications-based train control (TcCBTC) system is a new solution for urban transit signalling. Compared to traditional train control systems, the on-board equipment is becoming more powerful and more complex. Due to its safety-critical nature, specialised technologies must be adopted to guarantee the safety of the system. To address the safety verification difficulty of the control logic for the new system, this study presents an innovative topology-based method for guaranteeing the train control safety. First, a railway network is described as a metric space, and then, topological spaces are introduced to express the movement authority and train trajectory. On the basis of the topological description, the safety rules are checked by performing a series computation of topology theorems. Finally, a case study has been carried out on a real metro line in China. The result shows that the proposed method strictly meets the safety verification and achieves excellent performance.

The training of deep convolutional neural network (CNN) for classification purposes is critically dependent on the expertise of hyper-parameters tuning. This study aims to minimise the user variability in training CNN by automatically searching and optimising the CNN architecture, particularly in the field of vehicle logo recognition system. For this purpose, the architecture and hyper-parameters of CNN were selected according to the implementation of the stochastic method of particle swarm optimisation on the training–testing data. After obtaining the optimised hyper-parameters, the CNN is fine-tuned and trained to ensure better network convergence and classification performance. In this study, a total of 14,950 vehicle logo images are divided into two independent training and testing sets. In addition, these images are segmented coarsely, thus the requirement of precise logo segmentation is obviated in this work. The learned features of the CNN were sufficiently discriminative to be classified using multiclass Softmax classifier. With implementation using a graphics processing unit (GPU), the computation time of the proposed method is acceptable for real-time application. The experimental results explicitly prove that the authors’ approach outperforms most of the state-of-the-art methods, achieving an accuracy of 99.1% over 13 vehicle manufacturers.

Widespread adoption of electric vehicles (EVs) relies on a dependable public charging station (CS) network. CS locations should assure that vehicle users can reach the CS within the EV driving area. This study introduces a technique for optimal location and sizing of fast CSs (FCSs) that considers transportation loss, grid power loss and build-up costs. Google Maps API, battery state of charge, road traffic density and grid power losses are considered in the suggested method. A recently introduced binary lightning search algorithm is also implemented as an optimisation technique for FCS planning. The capability of the suggested method was tested in an urban area. Results reveal that the suggested technique can obtain the optimal location and sizing of FCS that can aid EV drivers, FCS builders and the utility grid. Furthermore, the suggested method obtained more realistic results compared with the traditional methods.

This study presents and tests the accuracy of a low-cost triboelectric sensor for speed measurement and weight estimation of passenger vehicles in motion. Polytetrafluoroethylene and aluminium films are employed as sensing elements for generating voltage pulses due to movement of vehicles in real time. A number of road tests by 880 and 1020 kg cars in the speed range from 10 to 60 kmph show that the sensor is effective for measuring speeds with more than 95% accuracy and 3% mean absolute percentage error. For weight estimation, it is found that the voltage signals obtained from sensor due to impact between the tires of the vehicles and sensor varies linearly with speed ensuring the reliability of the sensor. On road experimental results reveal that the weight can be predicted from voltage signals by maintaining a database of different class of vehicles. The results and the overall concept indicate that the triboelectric sensor and associated wireless setup is promising for smart traffic-monitoring applications at a very low cost.

To identify any aircraft in the world, it is sufficient to read its registration number. This number is a unique identifier, and offers valuable information, in the same way a car registration number does. In this work, the authors present the results of their feasibility study towards a simple, yet very efficient and effective system to identify aircrafts using video-optical character recognition acquired by off-the-shelf cameras. They used several videos under realistic conditions at the Heraklion airport during high season and they achieved very promising results. They claim that there is much room for the development of a low-cost airport surface monitoring system based on standard cameras, which can complement high-cost radars.

Public bicycle sharing programmes (PBSPs) have become increasingly popular across many urban areas in China. Hangzhou PBSP is the world's largest and forms this case study. The management of this large inventory of bicycles is a particularly challenging issue with the goal to ensure the demand for bicycles is met at all times across the network. To this end, an efficient scheduling approach is needed with the capacity to guide the redistribution of bicycles across the self-service stations. Drawing on 7 years of disaggregate trip data, this study first captures the usage dynamics across both space and time to extract the candidate stations and redistribution periods for vehicle scheduling. A region partition method with K-means clustering is proposed to satisfy the real-time requirement of large-scale PBSPs' redistribution. Moreover, drawing on the variations in demand a back-propagation neural network short-term prediction model is computed to inform the necessary prospective redistribution of bicycles to ensure demand is always met. Finally, a vehicle scheduling model employing a rolling horizon scheduling algorithm is established and implemented in a GIS-based prototype system. The prototype is evaluated through its effectiveness and found benefit for following 18 months of practical operation in Hangzhou PBSP.

A lot of effort is put into improving the efficiency of transportation and the optimisation of the current traffic situations. Nevertheless, this will not be sufficient to deal with the increasing number of road users. Public transportation is an appealing economical solution, although its quality is dependent on the efficiency of traffic lights and congestions. The aim of this study is to investigate the impact of public transport (PT) priority on the performance of a single road intersection with two two-way crossing streets and a dedicated tram line. In this perspective, the authors set up a microscopic simulation experiment to evaluate different PT priorities. The study is applied to a road intersection in Ghent (Belgium) and all relevant inputs are based on real-life data. Overall, the total travel time of all actors advocate for traffic light control regulations that accommodate regular road users at the expense of PT and contradict the current priority settings of the signal control system. However, the passenger-dependent travel times give a slightly nuanced view advocating a delayed priority or even no priority for PT vehicles only during the evening peak.