Intercity Freight Transportation Research Articles

Effects of Distance and Reliability on Value of Time in Intercity Freight Transportation: An Adaptive Experiment in China

Road–rail container intermodal transportation is considered a solution to reduce the share of truck transportation in China. A modal shift from truck to intermodal alternatives requires a better understanding of freight mode choice behavior and improved estimations of the value of service attributes. This paper focuses on the effects of distance and reliability on the value of time (VOT). An adaptive experiment is conducted on potential customers of intermodal transportation in the Yangtze River Delta area, China. Multinomial logit (binary logit) and mixed logit models are estimated for eight specifications. The results show that shipper characteristics, commodity characteristics, and shipment characteristics significantly influence the mode choice behavior. Specifications with an interaction term between the logarithm of distance and transportation time perform better. The VOT of short-distance transportation is higher than that of long-distance transportation. The rate of VOT reduction decreases with increasing distance. In addition, incorporating the reliability variable in model specifications leads to a more homogeneous random parameter distribution of time and a lower VOT. This study helps intermodal operators to optimize product and design pricing strategies. Moreover, the proposed measures help to promote the modal shift from truck to road–rail container intermodal transportation.

Повышение безопасности дорожного движения в условиях профессионального подбора водителей с учетом психофизиологических качеств

The professional suitability of a driver in motor transport enterprises is often limited by medical indicators, age and experience. Having a driver’s license of category B or C, driving experience of at least 3 years and a candidate who has reached the age of 21, within a few months of training can become a bus driver. Taking into account the increased social responsibility of the bus driver’s professional activity, it is important to apply an integrated approach to recruitment. The relevance of the work is confirmed by the results of an experimental study of the influence of the level of development of psychophysiological qualities of drivers on accident rates. It was found that at the enterprise in which professional selection of drivers was carried out using psychophysiological testing, the accident rate is 62.5% less than at the enterprise in which testing was not carried out. The aim of the work is to improve road safety in the conditions of using a set of methods of professional selection of drivers, taking into account psychophysiological qualities. The paper uses methods of mathematical statistics, the content component for which was obtained using the software and hardware complex UPDK-MK “Driving School” JSC (Neurocom). As a result of the conducted research, the possibility of differentiating candidates for the position of driver by types of transport work at the stage of professional selection is proved, which helps to reduce potential driver errors, thereby increasing its reliability and driving safety. Of the total number of candidates, 58% showed a high level of psychophysiological qualities. The distribution of the shares of candidates by types of transport work has been established: urban freight transportation — 31%, intercity freight transportation — 16%, urban passenger transportation — 6% and intercity passenger transportation — 5%. The scientific novelty of the study is the share distribution of successful candidates for the driver’s position in the conditions of using a set of techniques that differ from the well-known ones in that at the stage of professional selection it allows differentiating candidates by types of transport work: urban and intercity, freight and passenger transportation. The practical significance of the study is to improve road safety through the use of a differentiated approach to the selection of drivers of motor transport companies, taking into account the transport work performed. Further research is aimed at creating a system for improving the psychophysiological characteristics of drivers and candidates for drivers.

МЕТОДИКА ПОДБОРА ВОДИТЕЛЕЙ С УЧЕТОМ ПСИХОФИЗИОЛОГИЧЕСКИХ ОСОБЕННОСТЕЙ ПО ВИДАМ ВЫПОЛНЯЕМОЙ ТРАНСПОРТНОЙ РАБОТЫ

The article presents an analysis of the process of interaction of a motor transport enterprise with a psychological service, the result of which is a study of the psychophysiological characteristics of the driver. Significant psychophysiological indicators have been determined that affect the efficiency of various types of transport work. A methodology has been proposed that allows differentiating drivers by the transport work performed: urban and intercity transportation, freight and passenger transportation based on a study of psychophysiological features.

MRSI: A multimodal proximity remote sensing data set for environment perception in rail transit

Rail transit is becoming a major mode of rapid urban and intercity passenger and freight transportation, and its safe operation is of great significance in safeguarding people's lives and properties and maintaining social stability. The current scheme of manual hazard monitoring in rail transit still remains potential safety risks. Accurate rail scene understanding is an essential step towards a smart train. Limited by the closeness of railway scenes, not much research has been conducted on the perception and understanding of rail transit. In view of the above, we propose multimodal remote sensing image (MRSI), the first multimodal proximity remote sensing data set for rail scene understanding. MRSI consists of 27k images collected from freight rail and metro following the pixel and box annotations labeled and checked manually. We used a variety of sensing devices mounted on locomotives to record track scenes under different lighting and weather conditions, including straight, curve, and fork during daytime, dusk, and nighttime, as well as under rainy days. We also include an additional infrared thermometer in the metro environment, propose a new image registration method after synchronous acquisition, and thus construct MRSI combining spatial and radiometric properties. With this data set, we can achieve segmentation of the track area and recognition of obstacles by sensing the environment in front of the train, which lead to rail scene understanding. MRSI is publicly available at https://zenodo.org/record/5732905#.YaPIpsdBwdU.

Temporal instability of truck volume composition on non-truck-involved crash severity using uncorrelated and correlated grouped random parameters binary logit models with space-time variations

With the growing demand for inter-city freight transport, proportion of trucks in the freeway traffic has been increasing in China and worldwide in the past decade. There have been serious safety concerns for the prevalence of truck in freeway traffic given its size and weight, and the interferences of unsafe maneuvers of trucks on other traffic. However, existing literatures mainly focus on the crash severity of truck-involved crashes only. It is rare that the effects of the presence of trucks (of various classes) on the crash severity of non-truck-involved crashes (i.e. not involving any truck) are studied. To account for the effect of unobserved heterogeneity, random parameters discrete outcome models have been deployed to model the crash severity. However, the possible correlations between random parameters are seldom considered. This study aims to investigate the role of the presence and prevalence of trucks of various classes on the severity of non-truck involved crashes, with which both the uncorrelated and correlated panel-level space–time-varying heterogeneities are considered. To account for possible temporal instability, the uncorrelated and correlated grouped random parameters binary logit models are established for each separate year, based on the comprehensive crash and traffic data of eight freeway segments in Shandong of China during the period from 2016 to 2019. 4,008 crashes are extracted and grouped with space (i.e. road segment)-time (i.e. time of the day) variations considered. Results indicate that the proposed correlated grouped random parameters model is superior to the uncorrelated grouped random parameters logit model in three out of the four years. Also, correlation in the heterogeneous effects between super-large truck volume and average speed is significant. Moreover, temporal instability is revealed for majority of the factors, to different extents, including the heterogenous ones and volumes of all truck types. Nevertheless, this study addresses and discusses the fundamental issues of the temporally unstable correlation in unobserved heterogeneity between possible factors in crash severity analysis.

Mobility and evaluation of intercity freight CO2 emissions in an urban agglomeration

Freight transportation contributes to increasing carbon emissions in the transportation sector. The CO2 emissions inherently caused by the intercity freight transportation between cities should be emphasized. This study first introduces the concepts of CO2 emission intensity (EI) based on the extraction of freight transportation information in the form of GPS trajectory data. A gravity theory-based intercity CO2 EI model is proposed, so that the driving forces of emission generation can be revealed. Then, we analyze the CO2 EI in terms of mobility characteristics, spatial autocorrelation, and influencing factors. Finally, the proposed method is applied to a typical case study of the Beijing-Tianjin-Hebei urban agglomeration for evaluation of an emission control policy. The findings provide a theoretical reference for analyzing the intercity connections within a regional urban agglomeration in terms of emissions, and support the implementation of feasible emission reduction strategies.

Prediction of railway switch point failures by artificial intelligence methods

In recent years, railway transport has been preferred intensively in local and intercity freight and passenger transport. For this reason, it is of utmost importance that railway lines are operated in an uninterrupted and safe manner. In order to carry out continuous operation, all systems must continue to operate with maximum availability. In this study, data were collected from switch motors, which are the important equipment of railways, and the related equipment and these data were evaluated with sector experience and the results related to the failure status of the switch points were revealed. The obtained results were processed with support vector machines and artificial neural networks, which are artificial intelligence methods, and machine learning was performed. In the light of this learning, a decision support model, which predicts possible failures and gives information about the root cause of the failures that have occurred, was developed. This model aims to ensure that the data obtained in each movement of the railway switch point are processed and the necessary corrective and preventive actions are communicated to the maintenance personnel; thus, failures are eliminated before they affect the railway operation and the solution process of the failures that have occurred is shortened. Considering the six switch points from which the data were collected, the experimental results were predicted with 24% RMSE error rates in the SVM method, while they were successfully predicted with RMSE error rates ranging from 2.4% to 6.6% in the ANN method. Therefore, it is observed that the ANN method is more appropriate in the implementation of the established model.

Spatiotemporal Patterns and Decomposition Analysis of CO2 Emissions from Transportation in the Pearl River Delta

Controlling and mitigating CO2 emissions is a challenge for the global environment. Furthermore, transportation is one of the major sources of energy consumption and air pollution emissions. For this reason, this paper estimated CO2 emissions by the bottom-up method, and presented spatiotemporal patterns by spatial autocorrelation methods from transportation during the period 2006 to 2016. It further analyzed the impact factors of CO2 emissions in the Pearl River Delta by the Logarithmic Mean Divisa Index (LMDI)decomposition method. The results indicated that from 2006 to 2016, total CO2 emissions increased year by year. Guangzhou and Shenzhen were the major contributors to regional transportation CO2 emissions. From the perspective of different transport modes, intercity passenger transport and freight transport have always been dominant in the past 11 years. The results indicated that aviation transport was the largest contributor, and that travel by road was the second one. The CO2 emissions generated by rail and water transport were much lower than those from aviation. Private cars became the main source of urban passenger transport CO2 emissions, and their advantages kept increasing. The results indicated that the spatial agglomeration trend feature was negatively correlated, and the further the distance, the more similar the attributes. The cumulative contribution values of population, economic development, transport intensity, energy intensity and energy structure were all positive values, while the cumulative contribution values of transport structure and emission factor were negative. The findings of this study offer help for the scientific understanding of those CO2 emissions from transportation, and for adopting effective measures to reduce CO2 emissions and for the development of green transportation.

Network Characteristics and Spatial Organization Optimization of Intercity Freight Transportation Connection

The spatial characteristic of a freight transportation connection network can provide a basis for regional logistics planning and freight transportation organization. The theory of spatial rescaling is introduced for the method of developing a freight transportation connection network to effectively extract the complex interrelations in intercity freight transportation connections. This approach is conducted by applying Geographic Information System (GIS) spatial analysis technology on the spatial organization process and hierarchical structure of a system to explore the dynamic features of a freight transportation connection network. Yunnan province is used as an example. Then, the correlational analysis method and complex network are used to optimize a spatial organization to strengthen transportation connection and improve network stability to conduct a comprehensive evaluation of the transportation factors. Results indicate that (1) spatial organization of a freight transportation connection network is a complex process that involves geographic concentration and preferential attachment interactions, and the control power of key nodes will transfer to the secondary nodes with a decrease of its hierarchy, causing a balance in the spatial differentiation of urban functions; (2) the scale, grade, and accessibility of transportation infrastructure have a weak effect on the intercity freight connections, and spatial organization optimization improves transportation capacity; and (3) the enhancement of survivability of key nodes in the core network will achieve the goals that balance the network connectivity and improve the stability of freight system preferentially.

Fine-grained vehicle emission management using intelligent transportation system data

The increasing adoption of intelligent transportation system (ITS) data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. In this paper, we demonstrate the effective use of ITS and other traffic data to develop a link-level and hourly-based dynamic vehicle emission inventory. Our work takes advantage of the extensive ITS infrastructure deployed in Nanjing, China (6600 km2) that offers high-resolution, multi-source traffic data of the road network. Improved than conventional emission inventories, the ITS data empower the strength of revealing significantly temporal and spatial heterogeneity of traffic dynamics that pronouncedly impacts traffic emission patterns. Four urban districts account for only 4% of the area but approximately 30%–40% of vehicular emissions (e.g., CO2 and air pollutants). Owing to the detailed resolution of road network traffic, two types of emission hotspots are captured by the dynamic emission inventory: those in the urban area dominated by urban passenger traffic, and those along outlying highway corridors reflecting inter-city freight transportation (especially in terms of NOX). Fine-grained quantification of emissions reductions from traffic restriction scenarios is explored. ITS data-driven emission management systems coupled with atmospheric models offer the potential for dynamic air quality management in the future.

The Dry Port as a Holistic Approach for the Relocation of City Gateways

Alexandria city has several types of gateways that are not fully supported with connections and corridors that maximize their efficiency, thus, creating additional pressure on metropolitan areas. In addition, the marine gates of Alexandria and Dekheila face a lack of efficiency in their capacity as a port. The aim of this study is to relocate the city gates and to find a mechanism of change, which is necessary for promoting the globalization of Alexandria through strengthening inter-city freight transport.

The Dry Port as a Holistic Approach for the Relocation of City Gateways

Alexandria city has several types of gateways that are not fully supported with connections and corridors that maximize their efficiency, thus, creating additional pressure on metropolitan areas. In addition, the marine gates of Alexandria and Dekheila face a lack of efficiency in their capacity as a port. The aim of this study is to relocate the city gates and to find a mechanism of change, which is necessary for promoting the globalization of Alexandria through strengthening inter-city freight transport.

Assessing Intercity Freight Transport Network Vulnerability: An Empirical Case in Taiwan

Global environmental change has led to frequent climatic extremes. Along with resource allocation and preevacuation measures, transportation systems are crucial in disaster response and recovery, particularly emergent disaster logistics and island rescue. Intercity transportation is vital infrastructure for modern cities, providing access between cities and balancing the development of urban and rural areas. This study defined the indicators of intercity freight transport vulnerability according to node centrality, link importance, zone accessibility, and exposure. The empirical results demonstrated that the Taoyuan living perimeters, which encompasses a major airport and port, and generates substantial operating revenue, is extremely vulnerable because of high exposure and low accessibility, and that freeways are more vulnerable than railways. According to the empirical results, three recommendations are provided, namely enhancing connectivity between nodes, shifting the exposure of links, and balancing the industrial structure among zones.

The evolution of U.S. rail freight pricing in the post-deregulation era: revenues versus marginal costs for five commodity types

There have now been over three decades of experience with rate-making freedom for all modes of intercity freight transport in the United States. Most evidence suggests that regulatory change has been beneficial for the rail industry and its users. Despite evidence of positive impacts of regulatory reform of U.S. freight transport, there is limited evidence related to long-term pricing trends by commodity in the deregulated era. Moreover, U.S. shipper groups have called for increased regulation of U.S. railroads, citing increased rates and profits, and monopoly pricing to “captive shippers.” This study estimates U.S. railroad revenue-marginal cost ratios for seven different commodities between 1986 and 2008. Interestingly, we find no significant increase in revenue-cost margins for commodities thought to be “most captive” (coal and chemicals), while finding large increases for some commodities thought to be “non-captive.” These results may provide insight into the impacts of regulatory reform in other countries, where there are similar concerns of equitable pricing and financial viability. They suggest that a move toward a more market-based pricing system can enhance railroad viability without harming those with fewer transport options.

Logistics Costs Based Estimation of Freight Transportation Demand

Many supply chain and fi nished goods distribution networks involve intercity freight transportation. Shipping customers secure transportation services by matching their requirements to available service in an effort to minimize their total logistics costs subject to service level constraints. Frequently, shippers' modal decisions are constrained by short-term capacity constraints restricting one of the available options, or gaps in shipper knowledge or carrier marketing programs. As a result, the observed traffic flows may not reflect the potential demand for the mode. Because the potential demand for a mode is not directly measurable, when planning road and rail capacity, governments and railroads cannot make accurate capacity planning decisions based on current traffic flows. The model developed here identifi es the potential demand for intercity full truckload and intermodal shipments over the most heavily utilized 75,000 shipment lanes in the western United States by estimating minimum total logistics costs by mode. These flows are compared with actual U.S. freight flows in order to determine the differences between observed flows and the model estimated potential demand. The results indicate potential demand for intermodal transportation is high; considerable freight volumes could be delivered with lower logistics cost by switching from truck to intermodal transportation. This evidence suggests that observed traffic flows and trends may not be a sound basis for planning freight transportation infrastructure in the United States.

Brazilian low-carbon transportation policies: opportunities for international support

Two transport policies with climate co-benefits for the intra-city and inter-city transport in Brazil are analysed. Both policies aim to shift transport from the road (cars in cities, trucks between cities) towards less energy-intensive transport modes (bus, underground, rail and shipping). The main motivations for these policies are domestic benefits, including reduced energy costs, fewer accidents, and reduced local congestion and pollution. Despite these apparent benefits, barriers and financing constraints limit the scale and scope of the policy formulation and are likely to further impact their implementation. While it is relatively easy to mobilize investment in transport logistics infrastructure at the national level, given the benefits for economic competitiveness for the country as a whole, it is more challenging to leverage funds to provide more efficient and integrated public transport systems at the city level. International cooperation can play a significant role in supporting domestic policies, and thus contribute to direct emissions reductions and lower-carbon growth trajectories through a modal shift. Policy relevance: A modal shift of freight and passenger transport would enhance the energy efficiency of transport services. In Brazil, like most developing countries, infrastructure constraints prevent such a modal shift. Barriers to implementing national policies can differ within a country as well as internationally; for example, financial constraints are far more severe for cities pursuing public transport projects than for federal projects on inter-city freight transport. This creates opportunities for international support to tackle specific barriers and thus help governments to deliver domestic policy objectives. In order to avoid merely subsidizing the transport sector and increasing transport volumes, international support has to focus on measures to facilitate a modal shift that underpins a broader national transport strategy.

The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system

Mode choice modeling is probably the most important element of transportation planning. It affects the general efficiency of travel and the allocation of resources. The development of mode choice models has recently witnessed significant advances in many fields, such as passenger and freight transport. A large number of mathematical models have been used to model the traveler’s choice of mode and destination and the shipper’s choice of mode, shipment size and supply market, among others. Such models are not only becoming almost intractable but also data intensive, difficult to calibrate and update, and intransferable. These models cover a wide range of mathematical complexity and accuracy. This paper describes a new approach to mode choice of intercity freight transport modeling using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The new approach combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The approach is found to be highly adaptive and efficient in investigating non-linear relationships among different variables. The adaptive neuro-fuzzy inference system model is tested on the freight transport market in Turkey, Germany, France and Austria by using information on the freight flows and their attributes. The ANNs and ANFIS models are more successful in the representation of the non-linear behavior of mode choice of intercity freight transport compared to the classical models.

A multi-modal supply–demand equilibrium model for predicting intercity freight flows

In this paper a new approach to intercity freight transportation system modeling is developed. Modeling formulation considers supply–demand equilibrium, where the demand side represents the behavior of shippers (cargo owners) and the supply side represents the behavior of carriers (transportation operators). Shippers decisions considered include choice of destination, mode, carrier for pure modes and transfer point for combined modes. Carriers take routing decisions over a multi-modal, multi-product and multi-operator network. A new mathematical formulation, not known before, is proposed to find consistent equilibrium solutions for modal O–D shipments, network flows and levels of service. Necessary conditions are deduced to show that the solutions obtained, from the mathematical formulations proposed, satisfy the behavioral principles assumed in each case. It is shown that special rationality conditions are required, with respect to fares charged and network routing decisions, to obtain consistent supply–demand equilibrium solutions. Sufficient conditions for the existence and uniqueness of solutions to diagonalized versions of the mathematical problem formulated are deduced. Finally, a general solution approach is proposed and an application example is developed to illustrate the characteristics of the model and solution algorithm.

Elasticities of mode choice probabilities and market elasticities of demand: Evidence from a simultaneous mode choice/shipment-size freight transport model

Abstract This paper presents empirical estimates of market elasticities of demand and elasticities of mode choice probabilities in the intercity freight transport market. Results are derived from a mixed discrete/continuous choice model of mode and shipment size. The mode choice component of the full model was specified as a binary probit function. The two modes considered were rail and regulated common carriers (full truck load). Data was drawn from the US Commodity Transportation Survey consisting of individual shipments of manufactured goods identified at the most disaggregate level. Results obtained in this study are compared with those obtained in previous studies, and areas of similarities and dissimilarities in the magnitude as well as interpretation of the results are highlighted. The own-price and cross-price elasticities of mode choice probabilities were found to vary from 1.44 to 1.88, and from 1.54 to 1.75, respectively. The market price elasticities of demand were found to vary significantly across commodity groups and geographic territories. Among the 40 market segments considered, the truck price elasticity of demand ranged between −0.749 and −2.525; the rail price elasticity of demand was slightly larger, ranging between −0.956 and −2.489; and the rail–truck cross-price elasticity of demand ranged between 0.904 and 2.532.

Freight Planning Typology

A layered architecture for freight transportation demand modeling entails the construction of a statewide freight transportation demand model by separately simulating traffic for one commodity at a time. Layers can then be added together to construct a comprehensive model that includes the most significant freight flows. Most state or regional economies are dominated by a few economic sectors, and models can be constructed for those sectors that generate the most freight traffic and/or are the most important to the regional economy. Freight traffic demand modeling in intercity applications is more likely to focus on economic development, local infrastructure improvements, maintenance, and similar policy and planning concerns than on system capacity issues. Thus, it is more important to understand changes in traffic growth by economic sector than as the composite of all freight traffic. This method is less data intensive and more easily understood by transportation professionals than previous approaches. The layered approach is therefore more likely to achieve the desired objectives than would general models, which attempt to forecast heterogeneous freight transportation demands simultaneously. This approach is demonstrated through a case study using the meat products and farm machinery industries in Iowa. Other commodities will be added in the future to complete a model of Iowa’s statewide freight transportation demand. A framework is presented for organizing and identifying planning goals, key issues, and predominant commodities for intercity freight transportation. Although examples are provided, specific recommendations addressing the full range of issues, data collection activities, tools, and urban applications are suggested for further study. A case study demonstrates the approach used for one issue, one mode, and two commodities, which could be repeated elsewhere for similar applications.