Traffic control approaches, in particular Variable Speed Limit (VSL), are often studied as solutions to improve the level of service on urban motorways. However, the efficiency of VSL strongly depends on the spatiotemporal arrangement of VSL zones. It is crucial to determine the lengths and locations of VSL zones for best VSL efficiency before deployment in a real system, as the optimal length of the VSL zone and its distance from the bottleneck directly affects traffic dynamics and, thus, bottleneck control. Therefore, in this study, we perform the analysis of different VSL zones lengths and their positions by using a closed-loop Simple Proportional Speed Controller for VSL (SPSC-VSL). We evaluate the different VSL zone configurations and their impact on traffic flow control and vehicle emissions in a SUMO microscopic simulation on a high traffic demand scenario. The results support the observations of previous researchers on the significant dependence of VSL zone placement on VSL efficiency. Additionally, new data-based (traffic parameters and vehicle emissions) evidence of the performance of the SPSC-VSL design are provided regarding the best placement of consecutive VSL zones for motorway bottleneck control not analysed in previous research.
The centrality of stations is one of the most important issues in urban transit systems. The central stations of such networks have often been identified using network to-pological centrality measures. In real networks, passenger flows arise from an interplay between the dynamics of the individual person movements and the underlying physical structure. In this paper, we apply a two-layered model to identify the most central stations in the Beijing Subway System, in which the lower layer is the physical infrastruc-ture and the upper layer represents the passenger flows. We compare various centrality indicators such as degree, strength and betweenness centrality for the two-layered model. To represent the influence of exogenous factors of stations on the subway system, we reference the al-pha centrality. The results show that the central stations in the geographic system in terms of the betweenness are not consistent with the central stations in the network of the flows in terms of the alpha centrality. We clarify this difference by comparing the two centrality measures with the real load, indicating that the alpha centrality approx-imates the real load better than the betweenness, as it can capture the direction and volume of the flows along links and the flows into and out of the systems. The empirical findings can give us some useful insights into the node cen-trality of subway systems.
Traffic flow forecast is critical in today’s transportation system since it is necessary to construct a traffic plan in order to determine a travel route. The goal of this research is to use time-series forecasting models to estimate future traffic in order to reduce traffic congestion on roadways. Minimising prediction error is the most difficult task in traffic prediction. In order to anticipate future traffic flow, the system also requires real-time data from vehicles and roadways. A hybrid autoregressive integrated moving av-erage with multilayer perceptron (ARIMA-MLP) model and a hybrid autoregressive integrated moving average with recurrent neural network (ARIMA-RNN) model are proposed in this paper to address these difficulties. The transportation data are used from the UK Highways data-set. The time-series data are preprocessed using a random walk model. The forecasting models autoregressive inte-grated moving average (ARIMA), recurrent neural net-work (RNN), and multilayer perceptron (MLP) are trained and tested. In the proposed hybrid ARIMA-MLP and ARI-MA-RNN models, the residuals from the ARIMA model are used to train the MLP and RNN models. Then the ef-ficacy of the hybrid system is assessed using the metrics MAE, MSE, RMSE and R2 (peak hour forecast-0.936763, non-peak hour forecast-0.87638 on ARIMA-MLP model and peak hour forecast-0.9416466, non-peak hour fore-cast-0.931917 on ARIMA-RNN model).
This study introduces a novel methodological frame-work for extracting integral vehicle trajectories from several consecutive pictures automatically. The frame-work contains camera observation, eliminating image distortions, video stabilising, stitching images, identify-ing vehicles and tracking vehicles. Observation videos of four sections in South Fengtai Road, Nanjing, Jiangsu Province, China are taken as a case study to validate the framework. As key points, six typical tracking algorithms, including boosting, CSRT, KCF, median flow, MIL and MOSSE, are compared in terms of tracking reliability, operational time, random access memory (RAM) usage and data accuracy. Main impact factors taken into con-sideration involve vehicle colours, zebra lines, lane lines, lamps, guide boards and image stitching seams. Based on empirical analysis, it is found that MOSSE requires the least operational time and RAM usage, whereas CSRT presents the best tracking reliability. In addition, all tracking algorithms produce reliable vehicle trajecto-ry and speed data if vehicles are tracked steadily.
Based on the existing safe distance cellular automata model, an improved cellular automata model based on realistic human reactions is proposed in this paper, which aims to reproduce the characteristics of congested traffic flow. In the proposed model, the stochastic noise param-eter is optimised by considering driving behavioural dif-ference. The relative speed, gap and acceleration of the front vehicle are introduced into the optimised stochastic noise parameter oriented to describing the asymmetric acceleration behaviour of drivers in congestion. The sim-ulation results show that an uneven distribution of accel-eration trajectories of vehicles experiencing congestion exhibited on the spatial-temporal diagram of the pro-posed model is reproduced. Based on the analysis of the NGSIM, compared with the model with traditional sto-chastic noise parameter, the vehicles that move accord-ing to the proposed model can follow more easily and more realistically. Then the actual gap of vehicles can be better reflected by the proposed model and the change of vehicle speed is more stable. Additionally, the traffic efficiency from two aspects of flow and speed shows that the proposed model can significantly improve the traffic efficiency in the medium high density region.
Ancillary services in air transport represent a set of services provided to passengers to choose from, enabling them to enhance their travel experience while accumu-lating additional airline revenue. Low-cost airlines pi-oneered the practice, but the separation of ancillary services from the basic service has become an intense-ly growing trend in the air transport industry over the last decade. This practice has enabled low-cost airlines to significantly reduce the price of the basic service. To remain competitive in an era of transparency provided by search engines, traditional airlines offer ancillary ser-vices in addition to the basic service. To meet the passen-ger’s needs, a whole range of ancillary services has been created. However, existing revenue management systems do not take this ancillary revenue into account when cal-culating reservation limits. If the airline knew that an in-dividual passenger is willing to pay more for ancillary services, the system would be able to adjust the availabil-ity of the service for that passenger during the booking process. A review of research on passengers’ willingness to pay for ancillary services is presented in the paper, as well as a review on research on the personalisation of ancillary services and challenges of integrating person-alised pricing into existing revenue management systems.
The sugar-energy sector is extremely important to the Brazilian economy, with several other production chains derived from it, generating some of the main products linked to food and energy sources. This study proposes an integration model for sugarcane harvesting logistics processes, focusing on optimisation of industrial plant production capacity. Dynamic modelling has been applied to study a broad range of the productive phases of the sugar-energy chain. This paper proposes indicators to evaluate the degree of efficiency of the production logistics processes. Preliminary results showed that phase times in the production logistics processes can be significantly reduced in the harvest phase. When analysed as a coordination-oriented flow having chained activities, the production logistics processes optimise the speeds and travel times during the harvest phase. The developed model uses data set of the production and logistics processes phases of a sugarcane industry. A future study will focus on more detailed and complex stakeholder behaviours based on the model proposed.
Passenger exchange coefficient is a significant factor which has great impact on the pricing model of urban rail transit. This paper introduces passenger exchange coefficient into a bi-level programming model with time differential pricing for urban rail transit by analysing variation regularity of passenger flow characteristics. Meanwhile, exchange cost coefficient is also considered as a restrictive factor in the pricing model. The improved particle swarm optimisation algorithm (IPSO) was ap-plied to solve the model, and simulation results show that the proposed improved pricing model can effectively re-alise stratification of fares for different time periods with different routes. Taking Line 2 and Line 8 of the Beijing rail transit network as an example, the simulation result shows that passenger flows of Line 2 and Line 8 in peak hours decreased by 9.94% and 19.48% and therefore increased by 32.23% and 44.96% in off-peak hours, re-spectively. The case study reveals that dispersing pas-senger flows by means of fare adjustment can effectively drop peak load and increase off-peak load. The time dif-ferential pricing model of urban rail transit proposed in this paper has great influences on dispersing passenger flow and ensures safety operation of urban rail transit. It is also a valuable reference for other metropolitan rail transit operating companies.
The goal of the paper is to investigate the impact of tire tread depth on road accident risk and to develop an accident rate prediction model. The state of 4288 vehicle tires using tread depth gauge was inspected and processed statistically. The tread depth of the most worn tire from each vehicle was registered for further analy-sis. Based on the collected data, a statistical tire tread depth model for an insurance company vehicle fleet had been developed. The conformity of the gamma distribu-tion to the data was verified upon applying the Pearson compatibility criterion. The paper provides the histo-grams of the frequencies of tire tread depths and the theoretical curves of the distribution density. The prob-ability of the accident risk depending on the tire tread depth (adaptive risk index) was calculated applying the formed distributions and risk index dependence on the tire tread depth for the inspected vehicle fleet. Accord-ing to the developed prediction model, an upgrade of the regulation for the minimum allowed tire tread depth by 2 mm (up to 3.6 mm) could reduce road accident risk (caused by poor adhesion to road surface) to 19.3% for the chosen vehicle fleet. Such models are useful for road safety experts, insurance companies and accident cost evaluation specialists by predicting expenses related to insurance events.
In 1991, the European Union decided on setting up a liberalised and single railway market. However, in the atomised European region, more than a half of railways can be designated as small railways. For the very reason of significant differences between the national railway systems, the EU legislation has laid broad grounds for track access charge (TAC) modelling, thus resulting in many different TAC models. Out of numerous papers in respect of TAC modelling, only a small number consider the specificities and the needs of small railways. The paper aims to answer the questions of how to design or set up an efficient TAC structure when it comes to small countries. Another objective is to answer how to develop a TAC structure allowing the infrastructure manager to manage its costs. The answers to these questions are provided through the case study of railway in Montenegro – small railways in the Western Balkans. The main contribution of this paper is in developing the TAC model based on the efficient ratio of the capacity and infrastructure wear and tear components.
Visualisation helps explain the operating mechanisms of deep learning models, but its applications are rarely seen in traffic analysis. This paper employs a convolu-tional neural network (CNN) to evaluate road network performance level (NPL) and visualises the model to en-lighten how it works. A dataset of an urban road network covering a whole year is used to produce performance maps to train a CNN. In this process, a pretrained network is introduced to overcome the common issue of inadequa-cy of data in transportation research. Gradient weighted class activation mapping (Grad-CAM) is applied to vi-sualise the CNN, and four visualisation experiments are conducted. The results illustrate that the CNN focuses on different areas when it identifies the road network as dif-ferent NPLs, implying which region contributes the most to the deteriorating performance. There are particular visual patterns when the road network transits from one NPL to another, which may help performance prediction. Misclassified samples are analysed to determine how the CNN fails to make the right decisions, exposing the model’s deficiencies. The results indicate visualisation’s potential to contribute to comprehensive management strategies and effective model improvement.
In search for measures to reduce greenhouse gas emissions from transport, insights into the characteristics of all sorts of trips and specifically trips by car are needed. This paper focuses on everyday leisure trips for social and recreational purposes. Travel behaviour for these purposes is analysed considering individual and household factors as well as properties of the trip, based on Swedish national travel survey data. The analysis reveals that everyday leisure trips are often of joint character and that the average distance travelled per person and day increases with, for example, income, cohabitation, children in the household and residence in rural areas. The result also shows that the studied characteristics vary between studied trip purposes, influencing the sustainability potential of a reduction in car use and suggested measures. For instance, the largest share of passenger mileage comes from social trips, whereas trips for exercise and outdoor life have the largest share of car trips below 5 km. Several characteristics indicate difficulties in transferring trips by car to, for example, bicycle or public transport due to convenience, economy, start times, company etc. The study indicates that there is a need to take a broader view of the effective potential.