In modern cities, the subway system plays an important role in carrying a large proportion of passenger transport. However, there still remain some issues on how to accurately identify the regional functions of subway stations. In this study, the authors propose an approach named FISS for identifying the functions of subway station regions based on semantics mining and functional clustering. First, they extract the passenger's travel patterns of each subway station based on the smart card transaction data and Shanghai subway network data and calculate the relative point of interest (POI) contents of each subway station by using Shanghai POI data, then feed the two above-mentioned results into latent Dirichlet allocation model for obtaining the mobile semantics and the location semantics separately. Furthermore, they carry out standardisation after combining the two semantics, then extract the functional characteristic vectors of subway stations by conducting sparse principal components analysis, and cluster these vectors by using the improved k-means algorithm. At last, they visualise the result after taking subway station's function identification by the interclass passenger flow transfer, the distribution of geographical function proportion and the similarity of inter-cluster. The results demonstrate the accuracy and efficiency of the proposed approach compared with other existing methods.

Motivated by the practical requirements for timetable evaluation in China and the fact that massive train record data are collected but remain largely underused, this study presents a data analytics approach for train timetable performance measures using automatic train supervision data. A data preparation process with data cleaning and matching methods is developed for further analysis. The data analysis consists of three components: a waiting time assessment method that uses visual headway mismatching degree to estimate the effect on waiting time; a process time estimation method that introduces spatiotemporal distribution and statistical techniques to mine the data and identify practical characteristics of dwell time and running time; and an arrival punctuality examination method which checks on-time arrival performance. The proposed data analytics approach is demonstrated through a case study of Shanghai Metro. Major findings on timetable performance, involving three aspects of timetable parameters, are presented. The relevant data analytics framework and findings have operational and planning implications for urban rail transit authorities and operators with regard to evaluating timetable parameters and improving the service quality.

Traffic flow prediction is an essential component of the intelligent transportation management system. This study applies gated recurrent neural network to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, their method improves predictive accuracy and also decreases the prediction error rate. To their best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under gated recurrent unit-based deep learning framework.

Sharing bike service is a new emerging public transportation, which has been the hottest topic for months. Sharing bike service provides flexible demand-oriented transit services for city commuters. However, as large amount of sharing bikes flood into big cities, problems caused by chaotic order of sharing bikes are emerging slowly. The authors aim to draw support from taxi trajectories to analyse current traffic condition and improve it with sharing bikes. In this study, the authors propose a traffic congestion finding framework, called CF. In CF, derived from the points density-based clustering method of inspiration, the authors propose a new clustering method (CF-Dbscan) and successfully applied it to the clustering of trajectories. A road network matching algorithm (CF-Matching) helps to match GPS points to road net even if points are in low-sampling-rate. They also employ a ranking feedback mathematical model to adjust the number of sharing bikes of different stations to meet people's demand and reduce redundancy. The first experiment proves that the proposed clustering algorithm performs better than traditional DBSCAN. Another experiment is conducted to verify the effectiveness of the proposed framework in reducing traffic congestion. The experimental results prove that with the proposed framework the authors can achieve the purpose of easing traffic congestion.

For a few years, route optimisation and efficient traffic flow are a big challenge, especially in a current situation when 54.5% population is living in the urban environment all over the world. At peak hours, the traffic jams in urban areas are frequent. Lots of works have been done in finding the shortest path to optimise the route to the destination in minimum time. However, moving vehicles toward the shorter paths causes a severe traffic jam in the city. Therefore, in this study, the authors proposed a framework to enhance the efficiency of the ant colony optimisation (ACO) algorithm to optimise the vehicular traffic, i.e. named as smart traffic distribution ACO. It helps to optimise the route and city traffic efficiently while avoiding congestion in all circumstances using up-to-date city traffic data. Their proposed framework finds the optimal path in such a way that the traffic flow on each road remains normal. The detection of congestion on the road at an early stage and even distribution of traffic on all roads helps to achieve maximum flow, speed, and optimum density of the roads.

Real-time recognition of pedestrian details can be very important in emergency situations for security reasons, such as traffic accidents identification from traffic video. However, this is challenging due to the needed accuracy of video data mining, and also the performance for real-time video processing. Here, the authors propose a solution for fine-grained pedestrian recognition in monitoring scenarios using deep learning and stream processing cloud computing, which is called DRPRS (deep learning-based real-time fine-grained pedestrian recognition using stream processing). The authors design an improved convolutional neural network (CNN) network called fine-CNN, which is a nine-layer neural network for detailed pedestrian recognition. In DRPRS, a pedestrian in a surveillance video is segmented and fine-grainedly recognised using improved single-shot detector and several fine-CNNs. DRPRS is supported by parallel mechanisms provided by Apache Storm stream processing framework. In addition, in order to further improve the recognition performance, a GPU-based scheduling algorithm is proposed to make full use of GPU resources in a cluster. The whole recognition process is deployed on a big video data processing platform to meet real-time requirements. DRPRS is extensively evaluated in terms of accuracy, fault tolerance, and performance, which show that the proposed approach is efficient.

In emerging ride-on-demand (RoD) services, dynamic pricing plays an important role in regulating supply and demand and improving service efficiency. Despite this, it also makes passenger anxious: whether the current price is low enough, or otherwise, how to get a lower price. It is thus necessary to provide more information to ease the anxiety, and predicting the prices is one possible solution. In this study, the authors predict the dynamic prices to help passengers learn if there is a lower price around. They first use entropy of historical prices to characterize the predictability of prices in different locations and claim that different prediction algorithms should be used to balance between efficiency and accuracy. They present an ensemble learning approach to price prediction and compare it with two baseline predictors, namely a Markov and a neural network predictor. The performance evaluation is based on the real data from a major RoD service provider. Results verify that the two baseline predictors work well in locations with different levels of predictabilities, and that ensemble learning significantly increases the prediction accuracy. Finally, they also evaluate the effects of prediction, i.e., the probability that passengers could benefit from the prediction and get a lower price.

Detectors are challenged in providing stable and accurate information of the dynamic origin–destination (O–D) flows for real-time adaptive traffic signal timing operation. However, the dynamic O–D estimation technique is capable of providing a short-term turn-flow data for the signal-timing adjustment. Meanwhile, the real-time signal timing variations will affect the dynamic O–D flows in an actual network. Therefore, the dynamic O–D estimation and the real-time signal control closely interact with each. A combined model is proposed to dynamically calculate the signal adjustment answering the variation of the real-time O–D flows by minimising the accumulative queues in entering approaches. A case study was conducted to validate the proposed model and algorithms, and the result showed the traffic efficiency of the case study intersection was improved.

Energy efficient personal transportation requires fuel-efficient and route aware driving. Driving coaching systems can provide to drivers all the information and guidance that is needed to learn these skills. However, persuading drivers to change their driving behaviour is a challenging task. The authors identify functional, design, safety, and persuasive features for systems supporting fuel-efficiency. Moreover, they analyse how these features are supported by state-of-the-art systems targeting reduced fuel consumption. Finally, based on their analysis, they discuss open issues and opportunities for future development of fuel-efficiency support systems. The literature and the reviewed research in this study illustrate the needs for overall situation assessment and benefits of careful and multifaceted approach for systems design when it comes to eco-driving: an effective system will make use of a versatile design toolkit in order to obtain enduring behavioural results.

This study is among the early attempts to employ a surrogate-based optimisation (SBO) approach to solve the large-scale dynamic traffic assignment (DTA) calibration problem that is characterised by an expensive-to-evaluate and non-closed-form objective function. This paper formulates the calibration of the large-scale DTA model as a bi-level optimisation problem with a non-closed objective function such that it can only be evaluated through simulation. The Kriging surrogate model is adopted to construct the response surface between the objective value and the decision variables. The SBO approach first evaluates a number of initial samples, then fits the response surface and searches for the optima via an infill process. It reduces the number of large-scale DTA runs for evaluating the objective values and saves much computational time. For demonstrative purposes, a real-world large-scale DTA model in the state of MD is calibrated with the proposed SBO approach. After 400 initial points and 100 infill points, the SBO approach reduces the calibration matching gap from 29.68 to 21.90%. It is also presented that the proposed SBO is significantly faster than the genetic algorithm in searching for better solutions. The results demonstrate the feasibility and capability of SBO in DTA calibration problems.

Estimated travel time is a key input for many intelligent transport systems (ITS) applications and traffic management functions. There are numerous studies that show that fusing data from different sources such as global positioning system (GPS), Bluetooth, mobile phone network (MPN), and inductive loop detector (ILD) can result in more accurate travel time estimation. However, to date, there has been little research investigating the contribution of individual data sources to the quality of the final estimate or how this varies according to source-specific data quality under different traffic states. Here, three different data sources, namely bus-based GPS (bGPS) data, ILD data, and MPN data, of varying quality are combined using three different data fusion techniques of varying complexity. In order to quantify the accuracy of travel time estimation, travel time calculated using automatic number plate recognition (ANPR) data are used as the ‘ground truth’. The final results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. The results also show that even in dense urban areas, bGPS data, when combined with ILD data, can provide reasonable travel time estimates of general traffic stream under different traffic states.

This study investigates the problem of estimating on-street parking search time employing floating car data (FCD). The parking search path is modelled as a spiral around the destination. Model calibration is based only on data detected by tracked vehicles. The proposed methodology can be used both in real time to support user information and off-line to assess transport plans. In order to demonstrate its effectiveness for advanced transport modelling in urban areas, the results of a real-size application to the city of Rome are presented.

Timetable optimisation in metro systems is typically a multi-objective decision problem involving both social and passengers’ benefits. Based on the train operation and passenger demand data, this study develops a bi-objective timetable optimisation model to reduce both passenger time and carbon emission of train operation. Firstly, the cooperative scheduling rule of multiple trains within the same electricity supply section is analysed. The tractive energy consumption and utilisation of regenerative braking energy are calculated with a set of kinematical equations. The carbon emission is formulated according to the calculations of energy consumption. Meanwhile, a passenger time calculation function is established by analysing the real-world passenger demand data. Secondly, a bi-objective integer programming model with dwell time control is formulated, and a linearly weighted compromise algorithm and a heuristic algorithm are designed to find the optimal solution. Finally, a numerical example is presented based on the passenger and operation data from the Beijing Metro Yizhuang Line. The results show that the best found timetable can achieve a good performance on both carbon emission and passenger time in comparison with the currently used timetable.

A novel gravity-based measure method is proposed for accessibility analysis in transit networks. Trip characteristics of travellers (i.e. waiting time, transfer time, and times of transfer) and spatial distribution of transit stops are usually ignored in the traditional methods; thus, it is necessary and significant to taken them into consideration. The newly proposed method, which utilises service level factors together with transit stop reachability to measure the accessibility, is designed to make up this deficiency. Moreover, the principal component analysis method is innovatively used to determine the weights of service level factors. In order to evaluate the effectiveness of the proposed method, a case study is carried out in the real-world bus transit network in Beijing. Transit accessibility of the network is analysed and some possible reasons for the regions with poor accessibility are summarised. The analysis results are helpful in providing suggestions for policy makers and city planners, which may further improve the service level of bus transit networks. In addition, the concentration of transit accessibility in the selected network is further analysed. With the proposed measure method, the calculated equity results show that Beijing has an equitable bus transit network.

Impacts by over-height (OH) vehicles on bridges, commonly known as ‘bridge hits’, cause significant risk to safety and preservation of transportation infrastructure in the USA. Currently, available over-height vehicle detection systems (OHVDs) have specific site requirements, extremely high installation costs, and propensity for false alarms, which limit their field deployment to few locations. This study describes a new, enhanced LADAR-based OHVDs (L-OHVDs), which can be installed on the face of a structure to be protected and can measure the height of an approaching vehicle before the safe stopping distance from the structure. Built using off-the-shelf components and a patent pending optical design, it exhibits enhanced features like vehicle detection, actual height measurement, and collision prediction with no reported false alarms. The developed prototype has a detection range of 220 ft with height measurement accuracy (±0.66 inches) that is better than currently available OHVDs. This system has exceptional precision and is well suited to detect OH trucks and tractor trailers approaching a low vertical clearance bridge. With superior performance and cost-effective installation, the proposed L-OHVDs has the potential to reduce occurrences of ‘bridge hits’, thereby limiting consequences such as congestion and damages to bridge while sustaining safety of motorists.

This study evaluated the integrated strategy performance of vehicle route guidance and traffic signal control methods using traffic simulation. Based on the proposed optimal framework, three typical traffic signal control methods, including fixed signal control, actuated signal control, and regional coordinative signal control, were investigated via plugin applications, integrated with vehicle route guidance. The characteristics of vehicle route guidance approaches were captured using different guidance information updating frequencies and different user compliance rates. The average link occupancy was used as a representation of the network traffic states, and the network performance was measured by the total travel time. A total of 12,480 simulation runs were conducted. Based on the regression results, six integrated strategies were selected into a recommended integration strategy set. The transferability test shows that the performance of the integration strategy set is quite consistent.

Recently, content-centric networking (CCN) has been proposed as a promising solution for content distribution in vehicular ad-hoc networks (VANETs) owing to its named-data routing and in-network caching characteristics. In this network, the caching strategies are performed in the intermediate nodes, and even in the vehicular storage space. However, the typically existing caching strategies have indicated the low stored contents efficiency due to the peculiarities of VANET environment, like cache redundancy, high mobility, rapidly changing topology, and limited vehicular storage space. In this study, the authors proposed an efficient caching strategy in vehicle-to-vehicle scenario through CCN, which considers the requirements of different types of applications, the crucial features of data, and the peculiarities of the vehicular network (e.g. content popularity, cache occupancy, the stability link of the vehicles, and user's preference). Each vehicle makes its caching decision independently to improve the cache space and efficient use of the stored contents corresponding to the requirements of different application types. Simulation results validate that the proposed strategy outperforms other caching strategies in terms of reducing data retrieval delay, increasing cache hit ratio, and improving the cache performance on the vehicles.

Since autonomous four-wheel independently drive electric vehicles have the characteristics of parameter uncertainties, non-linearities and redundant actuators, trajectory tracking control for lane change of autonomous electric vehicles is regarded as a challenging task. A novel non-linear trajectory tracking control strategy is designed for lane changing manoeuvre. First, a dynamic trajectory planning strategy is proposed to update the desired trajectory according to the real-time information acquired through vehicle-to-vehicle communications. Second, a robust adaptive non-linear fuzzy backstepping controller is presented to produce the generalised forces/moment of autonomous electric vehicles, and the stability of this proposed adaptive controller is proven by the Lyapunov theory. Then, the quadratic optimisation goal function of tire energy dissipated power is constructed, and the optimal control allocation method is proposed to produce the desired longitudinal and lateral tire forces of autonomous electric vehicles. Finally, simulation results manifest that the proposed adaptive control strategy has the distinguished tracking performance.

In this study, the authors propose a novel and robust approach to control auxiliary tasks in vehicles using hand gestures. First, they create a three-dimensional video volume by appending one frame to other that captures the motion history of frames. Then, they extract features using histogram of oriented gradients on each video volume. These features are represented in the form of subspaces on Grassmann manifold. To improve the recognition accuracy, they map the data from one manifold to another manifold with the help of a Grassmann kernel. Grassmann graph embedding discriminant analysis framework is used to classify the gestures. They perform experiments on two datasets: LISA and Cambridge Hand Gesture in three different testing methods such as 1/3-subject, 2/3-subject and cross-subject. Experimental results show that their proposed model outperforms and is comparable with the state-of-the-art methods.

The express industry has developed rapidly, showing a picture of prosperity, but there are several problems behind its prosperity, such as lower efficiency, higher costs, more traffic pressures and increased disorder. To enhance operating efficiency, reduce delivery costs and ease both traffic pressures and disorder, the authors use the Shapley value method to establish a cost sharing model of terminal joint distribution for express enterprises. This model converts the proportion of income allocation into a cost sharing ratio and proposes a correction scheme of personal delivery service costs. The results of a case analysis show that the terminal joint distribution could reduce costs, such as wage costs and traffic costs, reduce both the costs of vehicle distribution and the number of vehicles distributed, and shortens the total distance travelled and time required for distribution. The model of cost sharing of terminal joint distribution for express enterprises could be fair. The cost sharing of terminal joint distribution depends on the personal level of the distribution service for express enterprises. They discuss the implications of the case analysis that jointly distributes costs among express enterprises along terminal routes for both the firms and for the emerging research on the joint distribution of costs.