Transportation System Performance Research Articles

An explanatory approach to modeling the fleet assignment in the global air transportation system

Airlines’ fleet assignment heavily affects the economic and ecological performance of the global air transportation system (ATS). Consequently, it is inevitable to include potential changes of the fleet assignment when modeling and assessing future global ATS scenarios. Therefore, this article presents a novel explanatory approach to modeling the fleet assignment in the global ATS. The presented approach is based on formulating and solving an optimization problem, which describes the fleet assignment in the ATS through a suitable combination of objective function and constraints. While the objective function combines both the airline and the passenger perspective on the fleet assignment, the constraints include additional operational and technological aspects. In comparison to the available global fleet assignment models in the literature, which rely on statistical approaches, the advantages of the presented approach via an optimization problem lie in the overall scenario capability and the consideration of explicit aircraft types instead of simplifying seat categories. To calibrate and validate our model, we use 10 years of historic flight schedule data. The results underline the strengths and weaknesses of the presented approach and indicate potential for future improvement.

Fuel Economy-Oriented Vehicle Platoon Control Using Economic Model Predictive Control

Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods.

Evaluating the Performance of Public Urban Transportation Systems in India

Evaluating the performance of public transportation systems facilitates operational improvement and strategic decisions. The purpose of this study was to evaluate the relative performance of 26 public urban transportation organizations in India using various criteria. We grouped these 19 criteria as Operations, Finance, and Accident-based. First, we evaluated the importance of these criteria groups using the Analytic Hierarchy Process (AHP). Then, we evaluated the organizations (Decision Making Units, DMUs) using various criteria within each criteria group using Data Envelopment Analysis (DEA). Finally, a Transportation Efficiency Number (TEN) was developed that quantified the overall performance of the DMUs considering the 19 criteria. Included is a discussion on the applicability of this approach, thus helping practicing managers understand the lacuna, if any, and set mutual benchmarks and benefits from the experience of others. This approach helps make strategic decisions for policy-making and achieves better results.

Development and application of a Bayesian network-based model for systematically reducing safety risks in the commercial air transportation system

Aiming to explore the nature of incidents in the commercial air transportation system, multiple causal factor events and result events, and their cause and effect relationships were retrieved from 7,265 incident cases in the Aviation Safety Reporting System (ASRS). The Bayesian network of commercial air transportation system safety risk (CATSSR-BN) was constructed on the basis of the hybrid algorithm of max–min hill-climbing (MMHC). The k-fold cross-validation of structure learning verified the accuracy and effectiveness of the CATSSR-BN. The measure of mutual information between causal factor event and result event was evaluated for determining its importance on safety performance of commercial air transportation system. Ten causal factor events with the highest values of mutual information across different result events were identified. A decision-making model was developed for optimizing resource investment in commercial aviation safety management. As a result, resource investment combination was determined on the combination of five causal factor events, which provided a systematic approach for reducing safety risk in commercial air transportation system and avoiding the occurrence of commercial aviation incidents with a high level of risk.

OPTIMIZATION AND STATE FORECASTING IN TRANSPORTATION SYSTEMS

Optimization of transport work is necessary in case of uncontrollable growth of the described costs. The process of optimizing transport costs at the enterprise begins with an analysis of the current logistics strategy and collection of recommendations for its correction. The following aspects of the transport system of the company are subject to analysis: the method of movement of goods; the choice of vehicle type and its specific model; the selection of the carrier company and other logistics intermediaries; the layout of the company’s storage terminals. An optimized transport system can reduce the cost of production and sales of products and increase staff efficiency.
 There are three main directions in which innovations will ensure the reduction of costs for cargo and passenger transportation: the choice of optimal vehicles, the selection of optimally located unloading and loading points, the analysis of the feasibility of own fleet of vehicles.
 The paper considers the possibilities of transport system management under the condition of external influences. An illustration of the algorithm scheme of solving the optimization problem related to the choice of initial actions is given. The possibility of using prognostic models is shown.
 The approaches proposed in the work can be useful in improving the performance of various transportation systems.

Transportation of Hybrid {textbf {MoS}}_2–{textbf {SiO}}_2/EG Nanofluidic System Toward Radially Stretched Surface

A stochastic computing approach is implemented in the present work to solve the nonlinear nanofluidics system that occurs in the model of atomic physics. The process converts the partial differential nanofluidics system with suitable level of similarities transformation into nonlinear systems of differential equations. For the construction of datasets, finite difference scheme (Lobatto IIIA) is applied through different selection of collocation points for nonlinear nanofluidics system having accuracy of order four. Lobatto IIIA has a strong point to tackle extremely nonlinear systems of ordinary differential equations in smooth way. For different scenarios, datasets are well trained through computing scheme to investigate the heat transfer and thermal performance of nanofluidic transportation system of nanofluids and hybrid nanofluids toward stretching surfaces with variation of Biot number, Nusselt number and skin fraction. Furthermore, the reliability, accuracy and efficiency are endorsed through various statistical analysis and graphical illustrations of proposed computing scheme.

Feeder Bus Reformation for an Urban Rail Project: The Case of Khon Kaen City, Thailand

The ability to use public transportation should be available throughout the whole service area and the public transportation network should be well connected. This research compared the potential coverage of a feeder bus network in support of urban rail transportation, as well as the impact of future transit network plans on public transportation accessibility in the city of Khoan Kaen, Thailand. The performance of the public transportation system was predicted based on multimodal transport and the completed urban rail public transportation plan, as projected in the year 2036, in order to fill gaps in the existing feeder bus network. The feasibility and characteristics of the route reformation policy concept should provide an effective feeder network for the urban rail system. A comparative study was conducted on stakeholder impact for a three-fold scenario: 1) separate individual lines for bus routes; 2) both forms of feeder bus networks (conventional and reformed); and 3) access to three designated utility areas from the entire feeder bus network. In this scenario, the most effective urban mobility support was provided by public facilities combined with a major roadway directly connecting to the designated positions. The time used on the extended bus route network increased by around 11% on average for the entire trip, while accessibility increased by approximately 67.75%, 47.9%, and 43.68% for the entire multimodal transport network. These analytical results make a significant contribution to future knowledge on urban transformation through urban mass transit projects. The contribution of land acquisition was significant. Also, the demand-responsive connection approach used in this study can be adopted to determine feeder bus reformation options, particularly in emerging economies.

An Improved Innovation Adaptive Kalman Filter for Integrated INS/GPS Navigation

The performance of transportation systems has been greatly improved by the rapid development of connected and autonomous vehicles, of which high precision and reliable positioning is a key technology. An improved innovation adaptive Kalman filter (IAKF) is proposed to solve the vulnerability of Kalman filtering (KF) in challenging urban environments during integrated navigation. First, the algorithm uses the innovation to construct a chi-squared test to determine the abnormal measurement noise; on this basis, the update method of the measurement noise variance matrix is improved, and the measurement noise variance matrix is adaptively updated by the difference between the current innovation and the mean value of the innovation when the measurement data is abnormal so as to reflect the impact degree of the current abnormal measurement data, thus suppressing the filtering divergence and improving the positioning accuracy. The experimental results show that the proposed algorithm can well suppress the filtering divergence when the measurement data are disturbed. The results demonstrate that the algorithm in this paper has improved adaptiveness and stability and provides a novel idea for the development of an intelligent traffic positioning system.

Passenger car equivalent travel time of a truck

• Differences in the average travel time of trucks compared to passenger cars by area type. • Average travel times differ by day-of-the-week and time-of-the-day. • Difference in the average travel time tends to decrease with an increase in the reference speed. • Average travel time of trucks could be up to 1.18 times that of passenger cars. The Highway Capacity Manual (HCM) recommends the use of passenger car equivalent (PCE) to account for the influence of trucks on the operational performance of roads. Researchers have derived PCE based on capacity, headway, queue discharge, and delay in the past. However, it is not clear if the influence of trucks on the operational performance of roads would be the same in terms of travel time. The focus of this research is to assess the influence of trucks on the transportation system performance from a travel time perspective. The travel time data for Mecklenburg County (urban area) and Iredell County (rural area) were gathered for the year 2017 and processed to generate sixty-nine datasets accounting for the area type, temporal variation, reference speed, and traffic condition. Ordinary Least Square (OLS) regression models were then developed to examine the relationship between the travel time of trucks (dependent variable) and the travel time of passenger cars (explanatory variable). The coefficients indicate that the average travel time (ATT) of trucks is greater than the ATT of passenger cars irrespective of the dataset used for analysis, except in two cases of Iredell County. The ATT of trucks could be up to 1.18 times the ATT of passenger cars in some cases. The area type, day-of-the-week (DOW), time-of-the-day (TOD), reference speed of the link, and traffic condition at the time of data collection all seem to influence the ATT of trucks at different scales compared to the ATT of passenger cars.

Modelling parking type choice behavior

• An econometric parking choice model is developed to investigate parking choice behavior. • We derive cruising and walking time valuations. • Drivers are willing to pay more for reducing cruising and walking time. • Trip purpose and turnover policy affects the choice of the type of parking. • Research on the Tunisian drivers’ behavior. Cruising for a parking space represents one of the major sources of urban congestion. Search for Parking generates additional urban traffic, energy consumption, vehicle emissions and time waste. Thus, it is essential to understand how drivers make decisions to select their parking lots. The estimation of models that reproduce parking driver's attitudes is crucial to improve the performance of parking facilities and transport systems. This study aims to understand the different behavioral considerations that rule the choice of drivers to choose one parking type among three alternatives: on-street parking, off-street parking and underground parking. For this purpose, we propose a multinomial logit model by using data obtained with a revealed preference surveys of drivers' parking type choices and associated factors in the city. Our findings provide authorities with additional perspectives for creating an efficient parking policy and associated land use planning. Indeed, to tackle the parking issue we had to pay attention to driver’s preferences regarding future parking supply options.

Condition-based monitoring of the rail wheel using logical analysis of data and ant colony optimization

PurposeIn this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.Design/methodology/approachApplication of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.FindingsResults achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.Originality/valueThe methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

Improving future travel demand projections: a pathway with an open science interdisciplinary approach

Transport accounts for 24% of global CO2 emissions from fossil fuels. Governments face challenges in developing feasible and equitable mitigation strategies to reduce energy consumption and manage the transition to low-carbon transport systems. To meet the local and global transport emission reduction targets, policymakers need more realistic/sophisticated future projections of transport demand to better understand the speed and depth of the actions required to mitigate greenhouse gas emissions. In this paper, we argue that the lack of access to high-quality data on the current and historical travel demand and interdisciplinary research hinders transport planning and sustainable transitions toward low-carbon transport futures. We call for a greater interdisciplinary collaboration agenda across open data, data science, behaviour modelling, and policy analysis. These advancemets can reduce some of the major uncertainties and contribute to evidence-based solutions toward improving the sustainability performance of future transport systems. The paper also points to some needed efforts and directions to provide robust insights to policymakers. We provide examples of how these efforts could benefit from the International Transport Energy Modeling Open Data project and open science interdisciplinary collaborations.

Connected automated vehicle impacts in Southern California part-II: VMT, emissions, and equity

Connected and automated vehicle (CAV) technologies are likely to have significant impacts on people's travel behaviors and the performance of transportation systems. This study investigates the impacts of CAVs from various aspects, including vehicle miles traveled (VMT), emissions, and transportation equity in Southern California. A comprehensive model is developed by incorporating the supply-side improvement of CAVs, a modified activity-based demand model supported by survey data, and a multi-class highway assignment model. The simulation results showed that VMT and emissions would increase by 10%, and CAVs could worsen travel equity across income groups. To reduce the negative impacts caused by CAVs, we proposed and evaluated a series of travel demand management policies. The results indicated that all policies help to reduce the VMT and emission growth, while their performances in enhancing travel equity vary across metrics including accessibility, travel frequency, and travel distance.

Stability and Resilience of Transportation Systems: Is a Traffic Jam About to Occur?

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

Visualization and Analysis of Transport Accessibility Changes Based on Time Cartograms

Visualization of the spatial distribution pattern of transport accessibility and its changes can be crucial for understanding and assessing the performance of transportation systems. Compared to traditional maps representing geographic space, time cartograms modify geographic locations and spatial relationships to suit travelling times and thereby emphasize time–distance relationships in time-space. This study aims to facilitate a better understanding of the evolution of the spatial distribution pattern of accessibility by presenting a novel visualization and analysis methodology based on time cartograms. This is achieved by combining a visual qualitative display with a quantitative indicator analysis from multiple perspectives to show transport accessibility changes. Two indicators, namely, the shortest railway travel time (STRT) and spatiotemporal con-version parameter (STCP), are proposed to measure accessibility changes. Our work consists of the construction of time cartograms, the analysis of indicators, and the use of multiple views to show changes in transportation accessibility from multiple perspectives. The methodology is applied on the railway data of Beijing and selected 226 cities in China and to analyze changes in railway accessibility in 1996, 2003, 2009 and 2016. The results show that the development of transportation technology has continuously shortened the travel time, the time-space is gradually compressed, However, the difference in transport accessibility is getting bigger and bigger because of the uneven transportation development speeds between the regions.

Time-Decoupling Layered Optimization for Energy and Transportation Systems under Dynamic Hydrogen Pricing

The growing popularity of renewable energy and hydrogen-powered vehicles (HVs) will facilitate the coordinated optimization of energy and transportation systems for economic and environmental benefits. However, little research attention has been paid to dynamic hydrogen pricing and its impact on the optimal performance of energy and transportation systems. To reduce the dependency on centralized controllers and protect information privacy, a time-decoupling layered optimization strategy is put forward to realize the low-carbon and economic operation of energy and transportation systems under dynamic hydrogen pricing. First, a dynamic hydrogen pricing mechanism was formulated on the basis of the share of renewable power in the energy supply and introduced into the optimization of distributed energy stations (DESs), which will promote hydrogen production using renewable power and minimize the DES construction and operation cost. On the basis of the dynamic hydrogen price optimized by DESs and the traffic conditions on roads, the raised user-centric routing optimization method can select a minimum cost route for HVs to purchase fuels from a DES with low-cost and/or low-carbon hydrogen. Finally, the effectiveness of the proposed optimization strategy was verified by simulations.

The Improved Precoding Method in the VLC-Based Intelligent Transportation System

Visible light communication (VLC) transmission system, combined with lighting and communication, has a great application value in the field of intelligent transportation. To efficiently improve the performance of VLC-based intelligent transportation system, an improved precoding scheme is proposed and experimentally verified. Partial data-carrying subcarrier (PDS)-precoding scheme can balance the signal-to-noise ratio (SNR) and combat frequency selective fading (FSF) in the VLC-based intelligent transportation system. For ∼3.52 Gb/s DMT-VLC system with 1.9-m free space transmission, the OCT-based PDS-precoding signal shows almost the same BER performance as full data-carrying subcarriers precoding ones. Compared with the conventional scheme, the BER is reduced from 2e-3 to 8.14e-5 by using the proposed PDS-precoding. PDS-precoding is a nice choice to degrade the unbalanced damage for data-carrying subcarriers (SCs) in the bandwidth-limited VLC-based intelligent transportation system.

Performance Dashboard Tool to Visualize Adaptive Resilience Maturity of Transportation Agencies

Incorporating adaptive resilience as a core value of transportation systems will equip transportation agencies to better address the increasing pace of future changes—both in climate and transportation system performance demands. Quantifiable measures are instrumental in incorporating adaptive resilience in agency organizational frameworks. This paper presents a dashboard tool that evaluates the maturity of adaptive resilience capabilities of a transportation agency, using an Adaptive Resilience (AR) Capability Maturity Model. A demonstrative example is presented to show the potential application of the dashboard tool and its implications for transportation system resilience planning. The example shows how the tool can facilitate identification of adaptive resilience capabilities that are at low maturity and thus may need prioritization for future resilience investments, and capabilities that are at a high maturity level and thus can be leveraged to enhance the lower-level capabilities. The AR performance dashboard tool may be used by transportation agencies to communicate their adaptive resilience efforts and maturity to stakeholders. It can also be used by practitioners to streamline and prioritize process improvements in adaptive resilience efforts.

Urban densification and sustainable mobility in a post-socialist city. Reconstruction of the science and business district development in Gdańsk

The transport land-use nexus lies at the core of the scientific debate on sustainable mobility. Researchers' insights suggest that densification can lead to better accessibility by reducing travel needs and distances, thus changing the modal split. However, lack of consistent transport policies, that accompany densification, can have side effects: increased congestion and pollution. Although analyses of the interaction between urban intensification and transportation planning have been conducted before, there is still a lack of knowledge of the form of that interaction in post-socialist cities. The process of emergence of the science and business district in Gdansk - a large, post-socialist city in Central Europe – provides an instructive subject for the analysis of this interaction. Descriptive and visual reconstruction of the land development and transport system performance, along with the analysis of strategic and planning documents, reveal inconsistencies in local policy and barriers to its implementation. A set of factors identified in the research indicates that the implementation of the accessibility and mobility strategies are deficient, leading to the disturbance of the balanced cycle model. On the one hand, these factors preserve an unfavourable modal split, but on the other hand, they may trigger bottom-up reactions and market initiatives.

Mixed network DEA models with shared resources for measuring and decomposing performance of public transportation systems

This study proposes a hybrid two-stage network model and a mixed network DEA with the shared-inputs model to jointly measure the efficiency and effectiveness of a metro transport system. Performance is determined by the hybrid two-stage network DEA with the shared-inputs model to account for the non-storable service feature. In contrast, the mixed network DEA model with the shared-inputs is used to describe the metro system's non-storable service, joint-product, and multi-subsystems features. Shared resources are analyzed by these two models to determine which common input resources are shared among multiple processes. A case study is conducted with the two models to evaluate and decompose the performance of a metro transit system. Results from the hybrid two-stage network and mixed network DEA models indicate that the performance measures or ranks differ significantly when using non-parametrical statistical tests. Transit system operators can gain more complete performance information on subsystems from the results of the mixed network DEA model than from the hybrid two-stage network model, enabling further improvements to their systems and sub-systems.