Junzhuo Li, Wenyong Li, Guan Lian

Data-driven forecasting methods have the problems of complex calculations, poor portability and need a large amount of training data, which limits the application of data-driven methods in small cities. This paper proposes a traffic flow forecasting method using a Nonlinear AutoRegressive model with eXogenous variables (NARX model), which uses a dynamic neural network Focused Time-Delay Neural Network (FTDNN) with a Tapped Delay Line (TDL) structure as a nonlinear function. The TDL structure enables the FTDNN to have short-term memory capabilities. At the same time, before the data is input into the FTDNN, the use of trend decomposition or differential calculation on the traffic data sequence can make the NARX model maintain long-term predictive capabilities. Compared with common nonlinear models, the FTDNN has structural advantages. It uses a simple TDL structure without the memory mechanism and the gated structure, which can reduce the parameters of the model and reduce the scale of data. Through the four-day data of Guilin City, the traffic volume forecast for five minutes is verified, and the performance of the NARX model is better than that of the SARIMA model and the Holt-Winters model.

2022 (Vol 34), Issue 6

Ahmed Jaber, Bálint Csonka

The purpose of this research is to investigate the effect of land use, built environment and public transportation facilities’ locations on destinations of bike-sharing trips in an urban setting. Several methods have been applied to determine the relationship between predicting variables and trip destinations, such as ordinary least squares regression, spatial regression and geographically weighted regression. Additionally, a comparison between the proposed models, count models and random forest has been conducted. The data were collected in Budapest, Hungary. It has been found that touristic points of interest, and healthcare and educational points have a positive impact on bike-sharing destinations. Public transportation stops for buses, trains and trams attract bike-sharing users, which has a potential for the bike-and-ride system. Land use has different effects on bike-sharing trip destinations; mostly as a circular shape variation within the urban structure of the city, such as residential, industrial, commercial and educational zones. Other variables, such as road length and water areas, form as constraints to bike-sharing trip destinations. Geographically weighted and spatial regression performs better than count models and random forest. This study helps decision-makers in predicting the origin-destination matrix of bike-sharing trips based on the transportation network and land use.

2023 (Vol 35), Issue 1

Pavle Bugarčić, Nenad Jevtić, Marija Malnar

Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transportation systems (ITSs). The high mobility of nodes in these networks leads to frequent link breaks, which complicates the discovery of optimal route from source to destination and degrades network performance. One way to overcome this problem is to use machine learning (ML) in the routing process, and the most promising among different ML types is reinforcement learning (RL). Although there are several surveys on RL-based routing protocols for VANETs and FANETs, an important issue of integrating RL with well-established modern technologies, such as software-defined networking (SDN) or blockchain, has not been adequately addressed, especially when used in complex ITSs. In this paper, we focus on performing a comprehensive categorisation of RL-based routing protocols for both network types, having in mind their simultaneous use and the inclusion with other technologies. A detailed comparative analysis of protocols is carried out based on different factors that influence the reward function in RL and the consequences they have on network performance. Also, the key advantages and limitations of RL-based routing are discussed in detail.

2022 (Vol 34), Issue 6

Ying Chen, Zhigang Du, Zehao Jiang, Congjian Liu, Xuefeng Chen

For urban extra-long underwater tunnels, the obstacle space formed by the tunnel walls on both sides has an impact on the driver's driving. The aim of this study is to investigate the shy away characteristics of drivers in urban extra-long underwater tunnels. Using trajectory offset and speed data obtained from real vehicle tests, the driving behaviour at different lanes of an urban extra-long underwater tunnel was investigated, and a theory of shy away effects and indicators of sidewall shy away deviation for quantitative analysis were proposed. The results show that the left-hand lane has the largest offset and driving speed from the sidewall compared to the other two lanes. In the centre lane there is a large fluctuation in the amount of deflection per 50 seconds of driving, increasing the risk of two-lane collisions. When the lateral clearances are increased from 0.5 m to 2.19 m on the left and 1.29 m on the right, the safety needs of drivers can be better met. The results of this study have implications for improving traffic safety in urban extra-long underwater tunnels and for the improvement of tunnel traffic safety facilities.

2023 (Vol 35), Issue 4

Laura Eboli, Maria Grazia Bellizzi, Gabriella Mazzulla

Evaluating air transport service quality is fundamen-tal to ensure acceptable quality standards for users and improve the services offered to passengers and tourists. In the transportation literature there is a wide range of studies about the evaluation of public transport service quality based on passengers’ perceptions; however, more recently, the evaluation of air transport service quality is becoming a relevant issue. Evaluating service quality in air transport sector represents a more stimulating chal-lenge, given the complexity of air transport system in re-gards to the other systems; in fact, air transport service is characterised by a great variety of service aspects relat-ing to services offered by the airlines and provided by the companies managing airports. The complexity of such a service requires a deep investigation on the methods adopted for collecting and analysing the data regarding passengers’ perceptions. We propose this paper just for treating these interesting aspects and to provide an ex-haustive literature review of the studies analysing ser-vice quality from the passengers’ point of view, where the opinions of the passengers are collected by the Customer Satisfaction Surveys (CSS). We decided to select papers published within the last decade (2010–2020) in journals indexed in important databases such as Scopus and WoS.

2022 (Vol 34), Issue 2

Meixian Jiang, Guoxing Wu, Jianpeng Zheng, Guanghua Wu

This paper constructs a berth-quay crane capacity planning model with the lowest average daily cost in the container terminal, and analyzes the influence of the number of berths and quay cranes on the terminal operation. The object of berth-quay crane capacity planning is to optimize the number of berths and quay cranes to maximize the benefits of the container terminal. A steady state probability transfer model based on Markov chain for container terminal is constructed by the historical time series of the queuing process. The current minimum time operation principle (MTOP) strategy is proposed to correct the state transition probability of the Markov chain due to the characteristics of the quay crane movement to change the service capacity of a single berth. The solution error is reduced from 7.03% to 0.65% compared to the queuing theory without considering the quay crane movement, which provides a basis for the accurate solution of the berth-quay crane capacity planning model. The proposed berth-quay crane capacity planning model is validated by two container terminal examples, and the results show that the model can greatly guide the container terminal berth-quay crane planning.

2021 (Vol 33), Issue 2