Traffic incidents in motorways: An empirical proposal for incident detection using data from mobile phone operators

This paper proves that mobile phone usage data is an easy to use, cheap and most importantly, reliable predictor of motorway incidents. Using econometric modelling, this paper provides a proof of concept of how mobile phone usage data can be utilised to detect motorway incidents. Greater Amsterdam is used here as a case study and the results suggest that mobile phone usage data can be utilised for the development of an early warning system to support road traffic incident management.     

The effect of urban growth on commuting patterns in Christchurch,New Zealand

The rising demand for residential development in the urban fringe of Christchurch, New Zealand, has led to an increase in car use and lengthening journeys, both of which have serious environmental implications. In light of this, the relationship between urban form and transport was investigated in Christchurch through the analysis of journey to work data from 1991 and 2001. Low density suburban areas were found to generate and receive the highest percentages of car trips in both 1991 and 2001. It was also found that the majority of work-trips involved suburb-to-suburb commuting, rather than to the centre of the city. Average trip length did not increase substantially over this period, although increases were apparent in separate modes. Multivariate regression analysis established that the key variable determining modal split and trip length was the distance the residence was located from the central business district. Results were similar when compared to larger cities elsewhere.     

Performance evaluation of China's air routes based on network data envelopment analysis approach

Air routes are among the most important elements of civil aviation transport. Airlines' operations are mainly dependent on the structure and layout of air routes. This paper first divides the production process of air routes into two stages, allocation and transport, based on air route operational characteristics. Then, two network data envelopment analysis (DEA) models are proposed to analyze the efficiency of the system, allocation, passenger transport, and freight transport of 477 air routes. The research result demonstrates that the different constraints on intermediate measure in the network DEA models do affect the air routes’ efficiency significantly; Most air routes have high allocation efficiency and passenger transport efficiency, while they have low freight transport efficiency. Furthermore, the efficiencies of 82 airports are also analyzed after aggregating the efficiencies of the air routes.     

Optimizing EV-based P&R subsidy policies for commuting corridor based on cross-nested logit model

The promotion of electric vehicles (EVs) is restricted by cruising range limitation and charging station deficiency. Given the mature development of Park and Ride (P&R) mode, which is used in many cities worldwide to attract more travelers to use public transit, a new travel mode, i.e., EV-based P&R is introduced as an alternative for commuters’ daily travel. This seems quite attractive to expand the use of EVs and further increase their market share. This paper aims to investigate the impact of EV-based P&R introduction on travel mode choice along commuting corridor, and further aid in the optimal subsidy policies decision for the government. A bi-level model is proposed to model the presented problem. The lower level describes commuters’ joint mode and transfer choice behavior through a cross-nested logit (CNL) model, while the upper level minimizes the system cost. A genetic algorithm is developed to solve the formulated model with a partial linearization algorithm for solving the lower level model. And a numerical example is then used to demonstrate the effectiveness of the methodology and illustrate how the network flow pattern reshapes due to the introduction of EVs into the P&R mode and the change of corresponding subsidy policies. As the results show, improving the EV hardware, applying the intelligent supporting service system, developing new technologies for EV fast charging, appropriately improving the parking space capacity, and increasing the parking fee of transfer stations near the central business district (CBD) are all helpful to save the social cost and promote the usage of EVs.     

Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users' travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers' residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns.     

The dynamics of modal shifts in (sub)urban commuting: An empirical analysis based on practice theories

A modal shift away from the private car onto low-carbon transport modes is an essential part of decarbonising the transport sector. The dynamics of modal shifts are, however, not yet well understood. In particular the interrelations between structural and individual dynamics require further investigation. Furthermore, a better understanding is needed of how new transport modes become integrated into existing mobility practices. In this article, we address these questions in a qualitative study of modal shifts in (sub)urban commuting in three major Swiss cities. We analysed the interview data by means of a qualitative content analysis informed by practice theories. We found that modal shifts can arise i) from dynamics related to the conditions of use of different transport modes, ii) the coordination of everyday mobility with other people, iii) the coordination of resources between different daily practices, and iv) from dynamics related to the intrinsic motivation of everyday mobility. We found that these different dynamics are intertwined and that to understand how modal shifts arise, they must be analysed conjointly, rather than in isolation. And we identified three patterns in how modal shifts play out, and which describe different ways in which the new transport mode becomes integrated into everyday mobility practices. The first pattern describes modal shifts which require no adaptation of existing commuting practices. The second pattern describes modal shifts after which a new everyday mobility routine must be built. And the third pattern describes modal shifts that coincide with a lifestyle change. We conclude by discussing the implications of our findings for decarbonising everyday mobility.     

An analysis of Brazilian flight delays based on frequent patterns

In this paper we applied data indexing techniques combined with association rules to unveil hidden patterns of flight delays. Considering Brazilian flight data and guided by six research questions related to causes, moments, differences, and relationships between airports and airlines, we evaluated and quantified all attributes that may lead to delays, showing not only the main patterns, but also their chances of occurrence in the entire network, in each airport and airline. We observed that Brazilian flight system has difficulties to recover from previous delays and when operating under adverse meteorological conditions, delays occurrences may increase up to 216%.     

Analysis on spatiotemporal urban mobility based on online car-hailing data

As an emerging travel mode, online car-hailing plays an increasingly important role in people's daily travel. Car-hailing data provide a new source to study human mobility in urban areas. This study focuses on identifying the distribution of regions with high travel intensity and the correlation between travel intensity and points of interest (POIs), based on the online car-hailing data collected in Chengdu, China. Firstly, the whole city area was divided into 16,100 uniform blocks and the number of pick-up and drop-off activities in each block was counted. Then, all POIs were categorized into 13 types and the number of different types of POIs in each block was counted. On this basis, the grade of travel intensity and POIs density in each block was identified according to the number of travel activities and POIs respectively. Finally, the correlation between the travel intensity and the POIs density was explored with ordered logistic regression. Experiment results showed that regions with high travel intensity are mainly distributed within the Second Ring Road, while those in the suburbs of city are usually the large transportation hubs, such as airports and train stations. Different types of POIs have different impacts on the online car-hailing travel intensity, and the density of traffic facilities has the greatest impact, including pick-up and drop-off, followed by density of scenic spot. The densities of service facilities and sports facility have an impact on the intensity of pick-up, while the impact on the intensity of drop-off is not significant. The density of company has no significant impact on the intensity of neither pick-up nor drop-off. These findings can contribute to a better understanding of online car-hailing travel activities and their relation with the urban space, and can provide useful information for urban planning and location-based services.     

基于小波变换的光突发交换网络业务流的研究

最近大量研究表明实际业务流存在分形特性,这对业务流的性能产生较大影响.针对光突发交换(OBS)网络边缘节点的汇聚业务流,首先在多重分形条件下利用小波变换对业务流进行尺度刻画.同时采用分块操作、块间变化的方法对业务流进行分析,通过深入研究发现均值和方差对业务流的性能影响较大.     

Short-term forecasts on individual accessibility in bus system based on neural network model

Precise forecasts on individual accessibility in bus system can help make policies to accommodate fluctuating bus travel demand and promoting social equity. In this study, we propose a three-stage method for short-term forecasts on individual accessibility in bus system based on neural network (NN) model. In the first stage, a NN model is designed to tackle the nonlinear mapping between passengers' bus trip appearances in historical periods and those in the predicted period. A rate function, which considers bus trip generation rates of passengers, is then applied using outputs of the designed NN model. In the second stage, probabilities of origin-destinations (ODs) chosen by passengers in the predicted period are calculated. In the third stage, land use information combined with results of previous two stages are used to obtain the individual accessibility in bus system in the predicted period. Compared to individual accessibility calculated by real data, it is found that the average errors of predicted results by the proposed method in weekdays and at weekends are only 8.37% and 10.13%, respectively. The results also demonstrate the capability of combining a NN model, traffic data and land use information to forecast the future spatial distribution of individual accessibility in transport system.     

Delimiting port hinterlands based on intermodal network flows: Model and algorithm

This study aims to propose a tangible approach to delimiting the probabilistic hinterland of a port of interest. We first build a geometric model for the probabilistic port hinterland based on intermodal network flows jointly using discrete choice analysis and geographical information of shippers. We further design an algorithm that can efficiently determine the hinterland boundaries using the sample approximation of shippers’ choice probabilities. We provide theoretical results that characterize the minimum computational effort required to achieve a certain degree of accuracy in the sample approximation. We also offer two numerical case studies to justify the proposed approach.     

Air express network design based on express path choices – Chinese case study

Due to the shortage of cargo aircraft, Chinese express companies have to provide transportation services with a hybrid method that consists of “rented bellies and self-owned cargo aircraft”. Because the bellies are rented from air passenger companies and because the cargo aircraft are owned by the express companies, it is important for the express companies to use their cargo aircraft as effectively as possible. This paper constructs a bi-level model to optimize the flight transportation network of an express company. The upper model designs the network and allocates the transportation capacity with the objective of minimizing the total transportation cost, and the lower model calculates the link flows in user equilibrium. Data from ShunFeng (SF) Express Company (China) are used to conduct the case study. Using the model outputs, the flows on links and at nodes are analyzed to illustrate the transportation and transshipment situations.     

Discovering temporal and spatial patterns and characteristics of pavement distress condition data on major corridors in New Mexico

Roadway networks, as part of transportation infrastructure, play an indispensable role in regional economies and community development. The high-quality pavement serviceability of these networks is essential to ensure safe, cost-effective daily traffic operations. In-depth analyses of network-wide pavement surface condition data are necessary inputs for optimal pavement design and maintenance, traffic safety enhancement, and sustainable traffic infrastructure system development. This study aims to investigate various pavement distress condition performance measurements and their correlations to better understand temporal–spatial characteristics of roadway distress based on pavement distress condition data collected in New Mexico from 2006 to 2009. Eight major corridors across various urban and rural areas were selected for analyzing pavement surface-distress conditions and discovering their intrinsic characteristics and patterns across both temporal and spatial domains. The results show that there are not strong correlations among different distress measurements, implying the rationality of the current pavement performance measurement protocol used by the state transportation agencies. Regression models were established and GIS-based spatial analyses were performed to extract temporal and spatial patterns of Distress Rate (DR) data. The model results illustrate significant correlations of the DR data on the same route between two consecutive years, which can be partially characterized by a Markov process. GIS-based spatial investigations also show unique features of pavement condition deterioration attributed to diverse geometric characteristics and traffic conditions, such as vehicle compositions and volumes and urban and rural areas. The research findings are helpful to understand the characteristics of pavement distress conditions more clearly and to optimize traffic infrastructure design and maintenance.     

Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

Traffic-related carbon dioxide (CO₂) emissions have become a major problem in cities. Especially, the CO₂ emissions induced by taxis account for a high proportion in total CO₂ emissions. The availability of taxi trajectory data presents new opportunities for addressing CO₂ emissions induced by taxis. Few previous studies have analyzed the impact of human trips on CO₂ emissions. This paper investigates trip-related CO₂ emission patterns based on individuals' travel behavior using taxi trajectory data. First, we propose a trip purpose inference method that takes into account the spatiotemporal attractiveness of POIs to divide human trips into different types. Further, we reveal the spatiotemporal patterns of CO₂ emissions from various types of trips, including temporal regularity and periodicity as well as spatial distribution of “black areas”. Finally, comparative analysis of CO₂ emissions for different kinds of trips based on trip behavior is conducted using three variables, namely trip distance, trip duration and trip speed. This study is helpful for us to understand how to make travel and cities more sustainable through modifying people's trip behaviors or taxi trips.     

Reducing airplane boarding time by accounting for passengers' individual properties: A simulation based on cellular automaton

The increase of air travel puts tremendous burden on airline companies. A time saving boarding strategy is required to improve the utilization of airplane boarding time and explore flexible time management strategies. Firstly, an improved boarding strategy is introduced by assigning individual passengers to seats based on the number of luggage they carry. Passengers with the most luggage board onto the plane first. To test the behavior of boarding strategies under different conditions, a sophisticated simulation environment based on cellular automata model is designed. Simulation results indicate that the improved boarding strategy shows an excellent efficiency and robustness comparing with other strategies.     

Insights into the fairness of cordon pricing based on origin–destination data

Origin–destination data are used to assess the vertical equity effects of a hypothetical road pricing zone in Canada's largest city. The assessment is based on the proportion of morning commuters affected by cordon pricing by virtue of residential location, trip destination, and travel mode. The overall findings for Toronto, Canada show that people with full-time employment and also those from higher income neighborhoods would be most affected by downtown road pricing; and this holds true when the population is broken out by gender, age group, household size and occupational class. The analysis also highlights that professionals, those who live in one- and two-person households, and those who are aged 65 and older would be disproportionately affected; those who work in manufacturing would be less affected. The equity effects of road pricing arise out of the commuting patterns of different sub-populations.     

Analysis of global marine oil trade based on automatic identification system (AIS) data

A fine-grained analysis framework of global marine oil trade based on AIS data is developed to address the existing problems of using statistical data to analyze oil trade without sufficient temporal and spatial resolution. The framework includes three modules: traffic route analysis, trade volume analysis, and trade network analysis. A ship cargo payload calculation (SCPC) model is proposed to take the draught, shape, and size of the vessel and seawater density into consideration. It calculates the oil trade volume of each oil tanker voyage as a unit. More than 3.4 billion global automatic identification system (AIS) records in 2017 are utilized to verify the proposed framework and achieve the following findings. The Middle East-Strait of Malacca-East Asia oil transport route is the busiest and largest trade volume route in the global marine oil trade. The oil trade volume of the world's top 20 oil-importing and oil-exporting countries calculated based on AIS data is strongly correlated to the Joint Organizations Data Initiative (JODI) statistics with the determination coefficient (R²) of 0.8798. More than 90% of the world's top 20 oil-importing and oil-exporting countries have more than five oil trading partners. The experimental results show that the proposed analysis framework has utilized the most minimal research object, every oil tanker's trajectory, to realize the fine-grained research of marine oil trade based on oil tanker flows analysis. The derived oil flows with directions and trade volumes provide the basis for constructing a directed weighted oil trade network.     

A time-period choice model for road freight transport in Flanders based on stated preference data

This paper presents one of the first models explaining the choice of time-period in road freight transport. Policies that would shift some fraction of the trucks from peak to earlier and later periods will contribute to the reduction of congestion. Therefore there is an increasing interest in modelling the time-period sensitivity of road freight transport to changes in travel time and cost by period. The model developed here is based on a stated preference survey amongst receivers of goods in Flanders and was implemented in the strategic freight transport model of the Flemish authorities.     

From bicycle sharing system movements to users: a typology of Vélo’v cyclists in Lyon based on large-scale behavioural dataset

Vélo’v have been available in Lyon, France, since 2005, and are one of the first major public bicycle sharing systems (BSS) implemented in Europe. With up to 7 million trips in 2013 and around 50,000 annual users plus occasional users, Vélo’v have increased bicycle use in the city by 50%. Analysing a database gathering both bicycle movement and user data for the calendar year 2011 provided by the operator, we concentrate our analysis on Vélo’v users. We characterise user mobilities and produce a user typology based on cluster analysis, relying on intensity and on annual and weekly temporal patterns. The proposed analysis, which creates user profiles from patterns, contributes to a close-reading of the interplay between BSS and other transports as well as to an improved understanding of conditions leading to a wider use of bicycles in cities.