Genetic algorithms in bus network optimization

This paper focuses on a new method to compute fitness function (ff) values in genetic algorithms for bus network optimization. In the proposed methodology, a genetic algorithm is used to generate iteratively new populations (sets of bus networks). Each member of the population is evaluated by computing a number of performance indicators obtained by the analysis of the assignment of the O/D demand associated to the considered networks. Thus, ff values are computed by means of a multicriteria analysis executed on the performance indicators so found. The goal is to design a heuristic that allows to achieve the best bus network satisfying both the demand and the offer of transport.     

Transition to a cyclable city: Latent variables affecting bicycle commuting

An understanding of the key factors influencing bicycle commuting is essential for developing effective policies towards a cyclable city. This paper contributes to this line of research by proposing a methodology for including cycling-related indicators in mobility surveys based on the Theory of Planned Behaviour (TPB), and applying an exploratory factor analysis (EFA) to evaluate the structure of latent variables associated with bicycle commuting. The EFA identified six cycling latent variables: Lifestyle, Safety and comfort, Awareness, Direct disadvantages, Subjective norm, and Individual capabilities. These were complemented with a latent variable related to habit: Non-commuting cycling habit. Statistical differences and regression analysis were applied with the cycling latent variables. The study also includes the relationship between objective factors and bicycle commuting, which reveals minor associations. This methodology was applied to the “starter cycling city” of Vitoria-Gasteiz (Spain). The results confirm that in this context – in transition to a cyclable city – safety and comfort issues are not the main barriers for all commuters, although more progress needs to be made to normalise cycling. A set of customised policy initiatives is recommended in the light of the research findings, including marketing campaigns to encourage non-commuting cycling trips, bicycle measures to target social groups as opposed to individuals, bicycle-specific programs such as “Bike-to-work Days”, and cycling courses.     

Analysis on line capacity usage for China high speed railway with optimization approach

The purpose for the analysis of capacity usage is to utilize the rail infrastructure in a more efficient and practical way. The practical and theoretical challenge of the rail capacity is its dynamics and uncertainty, which are common in China and elsewhere. Based on the capacity balance, a train service-demand intention set (TSDIS) at High-Speed Rail (HSR) line (t@l-TSDIS) is defined, which takes the number of trains, the average speed, the heterogeneity and the stability as the core elements for the capacity usage. For dynamics and uncertainty, we update the norm for capacity measure as the time needed to fulfill the task list t@l-TSDIS. Then we develop the objectives and constraints for the Mathematical Program for Line Capacity (MPLC), which aims at minimization of heterogeneity and running time as well as maximization of reliability. For solving MPLC, the Pareto Archived Evolutionary Strategy (PAES) and fuzzy logic penalty function are introduced. Furthermore we propose a rolling optimization tactic oriented by the practical problem, which combines the improved Pareto Archived Evolutionary Strategy (iPAES) with an interactive technique. In a case study, we apply the proposed ideas and methodology to Beijing-Shanghai HSR (BS-HSR) line much closer to the railway practice. By using the computer language C# to compile the Console program, Pareto optimized results for MPLC are achieved, including the standard and practical values for the heterogeneity indices, reliability indices and running time indices. We also discuss the sensitivity of the heterogeneity index. This research demonstrates that it is useful to analyze the line capacity usage for China HSR with the proposed optimization approach.     

Development of origin–destination matrices using mobile phone call data

In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.     

Traffic state estimation through compressed sensing and Markov random field

This study focuses on information recovery from noisy traffic data and traffic state estimation. The main contributions of this paper are: i) a novel algorithm based on the compressed sensing theory is developed to recover traffic data with Gaussian measurement noise, partial data missing, and corrupted noise; ii) the accuracy of traffic state estimation (TSE) is improved by using Markov random field and total variation (TV) regularization, with introduction of smoothness prior; and iii) a recent TSE method is extended to handle traffic state variables with high dimension. Numerical experiments and field data are used to test performances of these proposed methods; consistent and satisfactory results are obtained.     

Multiple criteria and fuzzy based evaluation of logistics performance for intermodal transportation

This paper presents a framework to evaluate the logistics performance of intermodal freight transportation. Fuzzy set techniques are applied to assess the logistics performance within the decision process of freight operators. Using a fuzzy‐based approach, fuzzy‐AHP is applied to assess the criteria by different judgment procedures. Consequently, fuzzy‐MCDM is used to assess operators' perception of the logistics performance via proper assignment of numerical scores. The subjective judgments for hierarchical criteria are transformed into fuzzy degrees of score. The methodology provides an alternative approach to facilitate the importance of a set of performance criteria. It can also entail use of improved corresponding parameters to develop a better freight transport system.     

A real time forecasting tool for dynamic travel time from clustered time series

This paper addresses the problem of dynamic travel time (DTT) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DTT are provided. DTT is dynamically clustered using a K-means algorithm and then information on the level and the trend of the centroid of the clusters is used to devise a predictor computationally simple to be implemented. To take into account the lack of information in the cluster assignment for the new predicted values, a weighted average fusion based on a similarity measurement is proposed to combine the predictions of each model. The algorithm is deployed in a real time application and the performance is evaluated using real traffic data from the South Ring of the Grenoble city in France.     

Identifying Governance Relationships Between Intermodal Terminals and Logistics Platforms

Abstract

Governance theory examines different ways of managing resources and relationships in order to achieve a desired outcome. This paper applies governance theory to intermodal terminals and logistics platforms, extending previous work on ownership to include different operational models. An inductive methodology is used to derive a typology of governance relationships from an analysis of the transport and logistics literature. The classification developed in this paper explores different kinds of integration that can help support growth of intermodal transport services. The understanding of transport governance is extended via three key relationships: first, between the logistics platform and the site tenants (therefore, encouraging consolidation and efficiencies that can boost rail services at the site); second, between the terminal operator and rail service provision (which can aid service planning and train loading factors); and third, between the inland site (either terminal, logistics platform or both) and port(s), (thus enabling better planning and efficiency of port rail shuttles).     

ELASTIC – A methodological framework for identifying and selecting sustainable transport indicators

There is significant reliance on sustainable transport indicators for monitoring and reporting progress towards sustainable transport. The selection of appropriate sustainability indicators presents a number of challenges however, not least because of the vast number of potential indicators available. To help address these challenges, this paper presents the Evaluative and Logical Approach to Sustainable Transport Indicator Compilation (ELASTIC) – a framework for identifying and selecting a small subset of sustainable transport indicators. ELASTIC is demonstrated with an application to the English Regions, UK.     

A comparison of two methodologies for selecting transit performance indicators

Two contrasting methodologies have appeared in the literature for selecting indicators to evaluate the performance of public transit firms. One methodology specifies the criteria that the selected performance indicators must satisfy. The other methodology requires the specification of operating objectives by the transit firm for the purpose of then selecting performance indicators. This paper compares the two methodologies and discusses the attributes of one methodology versus those of the other methodology. Also, a major difference in the premises of the two methodologies is demonstrated.     

A new statistical method of assigning vehicles to delivery areas for CO2 emissions reduction

Transportation CO₂ emissions are expected to increase in the following decades, and thus, new and better alternatives to reduce emissions are needed. Road transport emissions are explained by different factors, such as the type of vehicle, delivery operation and driving style. Because different cities may have conditions that are characterized by diversity in landforms, congestion, driving styles, etc., the importance of assigning the proper vehicle to serve a particular region within the city provides alternatives to reduce CO₂ emissions. In this article, we propose a new methodology that results in assigning trucks to deliver in areas such that the CO₂ emissions are minimized. Our methodology clusters the delivery areas based on the performance of the vehicle fleet by using the k-means algorithm and Tukey’s method. The output is then used to define the optimal CO₂ truck-area assignment. We illustrate the proposed approach for a parcel company that operates in Mexico City and demonstrate that it is a practical alternative to reduce transportation CO₂ emissions by matching vehicle type with delivery areas.     

Considering the financial ratios on the performance evaluation of highway bus industry

This paper tries to construct a performance evaluation procedure for highway buses with the financial ratio taken into consideration. First, a conceptual framework is redeveloped, based on the one created by Fielding et al ., to help form evaluation items and performance indicators involving both transport and finance aspects. Second, the total performance is divided into three major kinds of efficiency?production, marketing, execution?according to the cycle of operation activities. Third, to overcome the problems of small sample size and unknown distribution of samples, the grey relation analysis is used to select the representative indicators, and the TOPSIS method is used for the outranking of highway bus. In addition, a case study is conducted using four highway bus companies as example. The empirical result shows that the performance evaluation for highway buses could become more comprehensive if financial ratios are considered.     

Integrated scheduling of m-truck,m-drone,and m-depot constrained by time-window,drop-pickup,and m-visit using constraint programming

The idea of deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has vitalized this new research stream. One conceivable scenario of using a drone in conjunction with a traditional delivery truck to distribute parcels is discussed in earlier literature and termed the parallel drone scheduling traveling salesman problem (PDSTSP). This study extends the problem by considering two different types of drone tasks: drop and pickup. After a drone completes a drop, the drone can either fly back to depot to deliver the next parcels or fly directly to another customer for pickup. Integrated scheduling of multiple depots hosting a fleet of trucks and a fleet of drones is further studied to achieve an operational excellence. A vehicle that travels near the boundary of the coverage area might be more effective to serve customers that belong to the neighboring depot. This problem is uniquely modeled as an unrelated parallel machine scheduling with sequence dependent setup, precedence-relationship, and reentrant, which gives us a framework to effectively consider those operational challenges. A constraint programming approach is proposed and tested with problem instances of m-truck, m-drone, m-depot, and hundred-customer distributed across an 8-mile square region.     

Analysis of an O(N2) heuristic for the single vehicle many-to-many Euclidean dial-a-ride problem

We develop an O(N²) heuristic to solve the single vehicle many-to-many Euclidean Dial-A-Ride problem. The heuristic is based on the Minimum Spanning Tree of the modes of the problem. The algorithm's worst case performance is four times the length of the optimal Dial-A-Ride tour. An analysis of the algorithm's average performance reveals that in terms of sizes of single-vehicle problems that are likely to be encountered in the real world (up to 100 nodes) and in terms of computational complexity, the O(N²) heuristic performs equally well, or, in many cases, better than heuristics described earlier by Stein for the same problem. The performance of the heuristic exhibits statistical stability over a broad range of problem sizes.     

Designing a supply chain resilient to major disruptions and supply/demand interruptions

Global supply chains are more than ever under threat of major disruptions caused by devastating natural and man-made disasters as well as recurrent interruptions caused by variations in supply and demand. This paper presents a hybrid robust-stochastic optimization model and a Lagrangian relaxation solution method for designing a supply chain resilient to (1) supply/demand interruptions and (2) facility disruptions whose risk of occurrence and magnitude of impact can be mitigated through fortification investments. We study a realistic problem where a disruption can cause either a complete facility shutdown or a reduced supply capacity. The probability of disruption occurrence is expressed as a function of facility fortification investment for hedging against potential disruptions in the presence of certain budgetary constraints. Computational experiments and thorough sensitivity analyses are completed using some of the existing widely-used datasets. The performance of the proposed model is also examined using a Monte Carlo simulation method. To explore the practical application of the proposed model and methodology, a real world case example is discussed which addresses mitigating the risk of facility fires in an actual oil production company. Our analysis and investigation focuses on exploring the extent to which supply chain design decisions are influenced by factors such as facility fortification strategies, a decision maker's conservatism degree, demand fluctuations, supply capacity variations, and budgetary constraints.     

Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California

Using latent class cluster analysis, this paper investigates the spatial, social, demographic, and economic determinants of immigrants’ joint distribution among travel time, mode choice, and departure time for work using the 2000 Census long form data. Through a latent tree structure analysis, age, residential location, immigration stage, gender, personal income, and race are found to be the primary determinants in the workplace commute decision-making process. By defining several relatively homogeneous population segments, the likelihood of falling into each segment is found to differ across age groups and geography, with different indicators affecting each group differentially. This analysis complements past studies that used regression models to investigate socio-demographic indicators and their impact on travel behavior in two distinct ways: (a) analysis is done by considering travel time, mode choice, and departure time for work simultaneously, and (b) heterogeneity in behavior is accounted for using methods that identify different groups of behavior and then their determinants. Conclusively the method here is richer than many other methods used to study the ethnically diverse population of California and shows the addition of geographic location and latent segment identification to greatly improve our understanding of specific behaviors. It also provides evidence that immigrants are as diverse as the non-immigrant population and transportation policies need to be defined accordingly.

The economic impact of logistics infrastructure: the case of PLAZA – the Zaragoza Logistics Platform

Abstract

The economics literature offers many examples of studies of the economic impact of transportation infrastructure such as highways, ports, and airports; however, little attention has been paid to other types of infrastructure such as logistics platforms. This article is a first assessment of the economic effects of the largest logistics platform in Europe – PLAZA, the Zaragoza Logistics Platform – located in Spain. More precisely, this paper analyzes the economic significance of PLAZA on the economy of the Aragon region where it is located. The methodology is based on the application of input–output analysis to the final demand of logistics platform-based companies in a manner that is similar to well-established studies of the economic impacts of tourism on tourist expenditure. This makes it possible to quantify the various levels of economic effects (direct, indirect, and induced) in terms of jobs, salaries, and wages, turnover, gross operating surplus (GOS), and gross value added (GVA). The conclusions reveal the economic importance PLAZA has for the regional economy of Aragon, representing just over 2% of total GVA and 3% of turnover.     

Airport management: a strategic approach

In this paper a Dynamic Balanced Scorecard (DBSC) is used for the main purpose of indicating strategy implementation avenues to managers so as to equip them with more efficient decision-making tools. To that end, Fuzzy Multicriteria Decision-Making methodology is used as a source of technical support in formulating a cause and effect system and fuzzy strategic indicators. This methodological instrument brings a strategic vision to performance analysis and is designed to furnish a tool for evaluating the impacts of management action on the BSC fuzzy indicators. The proposed analytical methodology is applied to Brazil’s seven main international airports.