Freight Trip Research Articles

Freight and Service Parking Needs in Historical Centers: A Case Study in São João Del Rei, Brazil

The objective of this study is to analyze the demand for loading and unloading parking spaces in the center of São João Del Rei, a historical city in the State of Minas Gerais, Brazil, through freight trip generation models. To generate the models, the number of employees is used as an independent variable. Results show that the historical center receives an average of 710 freight trips per day, which would require at least 43 spaces for loading and unloading. As the center has only eight such spaces available, representing 18% of total demand, this study proposes new locations and suggests transportation demand management measures that could be used in conjunction with the allocation of new parking spots.

Sharing of trips before electrification of fleet: a costeffective solution for reducing the environmental impact of urban freight transport in developing countries

Various research efforts are undertaken to reduce the ill-effects of urban freight transport on the city’s environment. Replacing conventional freight vehicles with electric commercial vehicles (ECVs) is considered as the most effective solution; however, this transition is facing several technological and infrastructural challenges. In developing countries, where cities are already struggling to manage with their present infrastructure, they would find it even more difficult. This paper highlights the opportunity for sharing of in-use freight vehicles as an immediate solution to reduce vehicular emissions. Case study of Surat city, India is taken to assess the use of small-sized commercial vehicles for urban freight transport. A detailed study has been carried out to determine the extent of under-utilized or unutilized capacities of these vehicles. Reductions in emissions due to the sharing of trips are estimated. Propositions are made to strategize and develop policies that promote sharing of urban freight trips.

COVID-19 Outbreak in Colombia: An Analysis of Its Impacts on Transport Systems

The global COVID-19 outbreak has demanded drastic actions and policies from the governments and local authorities to stem the spread of the virus. Most of the measures involve behavioural changes from citizens to reduce their social contact to a minimum. Thus, these actions influence individual activity patterns and transport systems in different ways. This paper studies the short-term impacts on the transport system caused by the different policies adopted by the Colombian government and local authorities to contain the COVID-19 spread. Using official and secondary data concerning the seven most populated cities in Colombia, we analyse the impacts on three components of the transport system: air transport, freight transport, and urban transport. Results show that national policies and local decisions have decreased the demand for motorised trips across the cities, diminishing congestion levels, reducing transit ridership, and creating a reduction in transport externalities. The country banned air transport for passengers and only allowed air cargo for medical and necessary supplies, which will have negative consequences for the economics of the airline industry. During the first three months of the COVID-19, freight was the most resilient transport component. However, freight trips diminished around 38%, affecting mainly the supply chain of nonessential products. During the pandemic, governments need to provide subsidies to maintain the system supply to avoid crowdedness and promote active transport by allocating less-used street space to cyclists and pedestrians. In the short term, transportation service providers will face a financial crisis, deepened by the pandemic, which will require government assistance for their recovery.

Incorporating spatial interactions in zero-inflated negative binomial models for freight trip generation

This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities.

Development of Trip Generation Model: Case Study of Makarpura GIDC

Transportation planning is a basic piece of transportation designing, which incorporates four fundamental procedures viz, trip generation, trip distribution, model split, and trip assignment. This paper focuses on the trip generation, which is one of the four basic transportation planning processes. Due to Urbanization and Industrialization freight movement plays a very vital role in the Indian scenario. Freight transportation is considered as a backbone of industries and its development as it is essential for bringing raw material and delivering finished goods to the market. Makarpura GIDC is a part of the industrial assets of Vadodara city, which generates huge freight trips in Gujarat. The traffic volume count survey has been conducted which includes all the routes of Makarpura GIDC, for prediction of future trips generation. Paper also includes a detailed classification of industries and various trip generation models by the multiple linear regression method, which takes into consideration particular types of industries. Also, a relationship between the dependent and independent variables has been derived from the scatter diagram.

Freight Data-Driven Research on Evaluation Indexes for Urban Agglomeration Development Degree

A reasonable comprehensive evaluation of the degree of urban agglomeration development is of great significance for its sustainable development. Although there are some studies on the evaluation of urban agglomeration development degree from the overall development level, only a few studies consider internal development equilibrium and internal interaction intensity. This work uses freight trip data as the main empirical data to establish three alternative evaluation indexes named the overall freight intensity, the internal freight intensity equilibrium level, and the internal freight interaction relative intensity to reflect the urban agglomeration’s overall development level, internal equilibrium level, and internal interaction level, respectively. Then, this work weights the above three alternative indexes to comprehensively evaluate the comprehensive development degree of 14 Chinese urban agglomerations. Finally, this work classifies these urban agglomerations into three stages according to the comprehensive evaluation values. The research results could help us to reasonably evaluate the degree of comprehensive development of urban agglomeration.

Exploring the impacts of an emission based truck charge in the Netherlands

In this paper we explore the possible impacts of a distance- and emission-class based truck charge in The Netherlands. Earlier studies suggest that shifts may occur towards heavy vehicles, due to the relative inefficiency of lighter vehicles types. However, these studies have not taken into account the effects of changes in shipment size, as a response to pricing measures. Also, these studies have not considered emission-class dependent charges. Thirdly, no empirical disaggregate models or studies are available for the Netherlands for this problem. We present a discrete choice model for the joint choice of vehicle type and shipment size, estimated on a large dataset of disaggregate carrier freight trip data. The model explains variations in vehicle type choice for different transport purposes and contexts (e.g. commodity type, long-haul, urban transport, to/from logistic nodes). The analysis of emission based truck charging schemes shows that substitution towards low emission vehicles can be expected within the same vehicle class. It is also not likely that the truck charge will lead to a significant increase of shipment sizes or to substitution between vehicle types: a distance based truck charge increases transport costs but inventory costs restrain a shift to larger shipment sizes. This result points to a limited capability of supply chains to absorb transport cost increases by logistics re-organisation.

Modelling and solving an integrated freight train scheduling and trip planning problem with hazardous materials

ABSTRACT This paper investigates the routing and scheduling of rail freight of hazardous materials with train scheduling decisions considering risk-spreading function. We consider the problem of minimizing the weighted sum of the cost of serving the commodities plus risk-spreading function using well-defined measures, either the maximum population exposure, the difference between the maximum and minimum risk, or the mean absolute deviation of the risk while limiting the population exposure below the given risk thresholds. Non-linear-mixed integer programming models and heuristic method are proposed for preparing the shipment plans and determining the schedule of train services. Numerical examples are provided to study and analyse risk-spreading functions.

Air quality and fossil fuel driven transportation in the Metropolitan Area of São Paulo

In this paper, we use origin and destination mobility surveys and high-resolution assignment and emission models to study air quality and short-lived climate pollutant impacts related to on-road transportation in the Metropolitan Area of São Paulo (MASP). To begin with, we calculate transport carbon dioxide (CO2) emissions from fossil fuel-driven vehicles (light and heavy-duty) at spatial and temporal resolutions of 500 m and one hour, by means of traffic demand forecasting. These estimates, carry out for 2007 and 2012, are based on passenger and freight trips and the height of the atmospheric boundary layer, among other variables. These proxies depend also on ancillary parameters as particulate matter concentrations and dilution rates. In the second place, we evaluate the changes in CO2 emissions from the MASP (3%/year). Transport emission inventories combine mobility surveys and road network assignments with air quality data. In spite of using different methodologies, bottom-up road link assignments versus top-down vehicle activity-based and fuel consumption approaches, the estimated CO2 emissions are consistent with the Official São Paulo State's Inventory. This work found that the CO2 emissions in MASP were 10,044 and 11,503 t Ceq./day in 2007 and 2012 (73% light and 27% heavy vehicles), respectively. On-road emission patterns agree with the spatial-temporal variation of transportation journeys and corresponding passenger and freight network assignments. Temporal patterns, diurnal, weekly and monthly, were determined using traffic counts and congestion surrogates. The patterns were also crosschecked with average CO2 measurements, available for 2014 at the road (western area of MASP) and background sites (Jaraguá Peak). Kerbside road measurements showed two prominent peaks associated to the morning (437 ± 45 ppm) and night rush hours (435 ± 49 ppm), coupled to low values of boundary layer height (313 m) and dilution rate (329 m2/h). Background values (414 ± 2 ppm) were subtracted from on-road measurements to estimate excess CO2 (12 ± 8 ppm) directly attributed to vehicles. The inventory reflects the relationships between traffic patterns and emissions, and the developed methodology could be used to evaluate the impacts of forthcoming urban transport and emission control policies. In the future, our estimates will be verified with ground measurements of CO2 concentrations over a bigger monitoring network in the MASP.

A Multiclass Tour Flow Model and Its Role in Multiclass Freight Tour Synthesis

This paper summarizes the research conducted by the authors concerning the development of an analytical freight demand model that explicitly considers freight pickup and delivery flows by industry sector, the multiclass tour flow model (MC-TFM). The mathematical properties of the resultant model are investigated, including the convexity of the optimization model, the interconnection between the MC-TFM and the family of gravity models, and the MC-TFM's elasticities. The MC-TFM is then integrated into a multiclass freight tour synthesis model (MC-FTS) that is capable of inferring freight tour demand on the basis of secondary data such as traffic counts and estimates of freight trip generation by industry sector. The ability of the resulting MC-FTS is tested by means of numerical experiments involving test cases.

Links between freight trip generation rates, accessibility and socio-demographic variables in urban zones

This paper proposes an assessment of the links between freight trip generation (FTG) rates and accessibility. First, the paper overviews the background, sets the context and motivates the research. Second, it presents the proposed methodology, which combines an FTG model, two accessibility indicators and a linear regression analysis to assess the relationships between freight trip demand and a set of socio-demographic variables including accessibility. The FTG modelling framework, adapted from previous works, allows estimating the number of freight trips with a small amount of standard data, even when no surveyed data is available. The two gravity accessibility indexes, one potential and one exponential, are defined in the continuity of recent freight accessibility works. To those indicators, a set of socio-demographic variables, including population, area or a zone (or density), are introduced. The relationships between FTG and all those variables are assessed via standard linear regression methods completed by the verification of the corresponding linear relationship hypotheses. Third, the framework is applied to the urban area of Lyon (France), where no urban goods survey data is available. Results show that potential accessibility seems to have a better correlation to FTG and could be a good decision support indicator when combined with the population as an explanatory variable. The population can be added to accessibility as an explanatory variable, the resulting models with two variables have a slightly lower accuracy but remains close to that of models with only accessibility as an explanatory variable. This work remains exploratory and finishes by proposing practical implications and further development lines.

Effects of Business Age and Size on Freight Demand: Decomposition Analysis of Indian Establishments

Freight studies have traditionally used business size measures such as employment and gross floor area as predictors of freight generation, giving very limited attention to the effects of an establishment’s age on freight demand. This study uses establishment-based freight survey data collected in seven cities of Kerala, India, to analyze the impacts of the age of an establishment on its freight demand. This is achieved by grouping the establishments into pre-specified classes with relatively homogeneous freight demand pattern. This classification is based on a data-driven a posteriori segmentation of industrial classes. These groups are further divided into sub-groups based on founding year of an establishment. As the business age increases, establishments grow in business size indicators and hence the changes in productivity can be either because of age or because of size or both. It is very important to separate the effects of these two indicators. The Blinder–Oaxaca decomposition method is used in this study to disentangle the effects of size and age on freight production and freight trip production. Age is found to be a fundamental driver of freight demand in younger establishments while the explanatory power of business size variables in explaining freight demand diminishes with age. The study findings illustrate the potential omitted variable bias that can occur when freight demand is estimated using business size indicators, without controlling for the business age differences.

Urban Freight Movement and Its Impact on Traffic Flow Pattern in Developing Countries

Surat is one of the prime cities for the manufacturing of textile products in the country, having 40% of the man-made fabric production in the country. The city produces 30 million meters of raw fabric and 25 million meters of processed fabric daily. Textile industry in the city has witnessed tremendous growth in the last decade, which has led to much transportation related changes in the city. The textile industry of Surat has different segments such as weaving unit, processing unit, value addition and trading hub for carrying out various processes. The segments are located as clusters or pockets in different parts of the city or urban area. Due to the dis-integrated nature of the industry here, it produces a lot of internal goods movement. The present study is focused on analyzing the characteristics of textile fright trip generated in the city due to the initial unit of textile industry i.e. weaving unit. Stated preference survey was conducted for 122 weaving units located in different parts of the city. Multi-linear Regression model for trip generation and quantity of cloth produced is developed for estimation of textile freight trips and quantum of production. This study will definitely help the planner to identify the strategic location of the textile industry and allied supporting industry. This study further helps in understanding the trip generation and attraction in the city which affects on routine traffic as well.

Alternative Modelling to Estimate Freight Trip Generation

Freight trip generation models essentially use the least-squares estimation technique, losing in part the power of weighted least squares, least squares techniques in two stages, and instrumental variables that could address the complexities of the transport phenomenon that has repercussions in the explanatory and dependent variables, as well as in the hypotheses related to the behaviour of the estimation error. In this sense, this paper presents an alternative modelling to estimate freight trip generation using different techniques dealing with the problems arising from linear regression and obtaining efficiency in the estimation models. Also, an exercise considering warehouse freight trip generation modelling is presented using the traditional technique of least squares and comparing the results with least-squares weighted, least-squares techniques in two stages and instrumental variables. The results indicate that the proposed modelling improve linear models to predict freight trip generation estimation, guaranteeing better adherence than traditional modelling.

Characterization of Freight Trip from Textile Powerloom Units – A Case Study of Surat, India

Surat, a city located in western part of India is known for its textile industries. The city produces 40% of the total man-made fabric production in the country. There are close to 700,000 weaving machines producing approximately 30 million meters of raw fabric every day. Almost 650,000 of these weaving machines are powerlooms or shuttled looms. Powerlooms are widely used because they are cheaper, needs lesser space and offers variety of weaving options. A large variety of raw fabric is produced based on its utility which is sent for its further processing works to the processing units. A textile market area located on the ring road is a hub for trading of various textile goods. A large number of goods movement trips occur from various powerloom units to the market and other processing units in the city. With 25000 weaving units located in different parts of the urban area, 320 processing units and 140 wholesale trading market complexes, the internal or intra-city textile goods movement becomes very complex. In this study freight trip generation characteristics of powerloom industry is studied here and a model is developed to forecast the number of freight trips from various powerloom units clusters so as to estimate the overall flow of freight vehicles at the network level

Combining Macro- and Microscopic Approaches to Model Commercial Transport Demand in an Urban Area

Demand modeling of commercial transport has lagged behind private passenger demand modeling as a result of insufficient data sources and the complex types of movement. At a supra-regional level, commercial trips are usually conducted by heavy trucks. However, studies in urban areas show that only about 40% of all commercial trips are conducted by heavy vehicles, while the other 60% is conducted by light vehicles, indicating that this is not freight travel. Even though commercial transport has been somewhat regarded in recent research, most models focus on either freight trips or trips conducted to provide a service. However, for assessing policy sensitivity, all parts of commercial transport need to be considered.The model we present regards all aspects of commercial transport by assessing the parts separately. We distinguish between vehicles with variable and fixed daily. We further differentiate vehicles with fixed daily schedules into vehicles with short and long supply chains while focusing on the latter. To regard the entire supply chain of delivery vehicles we a combined macroscopic approach to obtain transit flows on a European level with a microscopic approach that is used to distribute the obtained packages of an urban area within said areas. The results of both the macroscopic and microscopic parts of the model are compared to traffic count data. The comparison of values show that the combination of macroscopic and microscopic model parts can model vehicles of commercial transport with a long supply chain, however, more attention should be regarded to the microscopic distribution.

Freight trip generation modeling and data collection processes in Latin American cities. Modeling framework for Quito and generalization issues

Abstract Ever-growing urban areas and global population movement towards urbanization lead to major concerns regarding urban logistics and last mile operations. In Latin America, the problem becomes critical since volatile emerging economies and unstable political situations, which are common in the region, introduce additional limitations for strong logistics solutions. In the city of Quito (Ecuador), traffic regulations only consider time-schedule restrictions for vehicle mobility without any other policies that benefit urban goods movement. This is in part because there is a lack of knowledge on freight flows, mainly related to the difficulties to retrieve data to that purpose. This paper proposes a freight trip generation analysis in Quito, based on a methodology included in “MIT’s Better Cities for Logistics Toolkit”, which defines specific zones observation and data collection campaigns. More precisely, a procedure combining observation-based and declarative data collection processes is proposed. First, the opportunities of combining both observed and declared data to characterize freight trip generation are addressed on the basis of a literature review. Main issues of combining establishment based surveys and observations are addressed to generalize the proposed framework. Finally, application implications in a transferability perspective to other Latin American countries are addressed.

Assessing the extent of modifiable areal unit problem in modelling freight (trip) generation: Relationship between zone design and model estimation results

There is a growing interest in incorporating spatial indicators into freight demand model systems. The indicators are measured for different areal units (e.g., census tracts or block groups) and are often used as proxy variables or aggregation layers. Model estimation results vary according to the choice of these areal units and an analyst is thus confronted with a popular decision challenge termed as ‘modifiable areal unit problem’ (MAUP). The variability in results due to MAUP arises since areal units can be modified in theoretically infinite ways (in terms of shape, orientation and size) and magnitude of aggregation loss in information will vary for each alternative zoning system. In effect, how well the zonal (aggregated) characteristics can describe the establishment-level (disaggregated) observations is inversely related to MAUP effects. Little is known, however, about the extent of MAUP effects in freight generation (FG) models and freight trip generation (FTG) models. This study diagnoses the implications of MAUP effects in FG and FTG models by designing alternate zoning systems (by means of different zonal variables and clustering techniques) and assessing the sensitivity of model estimation results within a framework of comparative analysis (by means of hierarchical linear models). Study results assess the presence of MAUP as alternate zoning systems resulted in wide variation in the estimated coefficients for zonal characteristics (e.g., industrial area, land value, number of establishments, distance to primary arterial) in terms of magnitude, statistical significance, and even in the direction of association (sign of the coefficient). The implication of this finding is that an analyst may design different or even counterproductive policy instruments based on the way data is aggregated to capture the role of land-use, spatial effects and built-environment in influencing freight travel patterns. MAUP effects are also found to be dependent on the metric in which freight is measured (i.e., FG or FTG) and direction in which flow is measured (i.e., production or attraction). Overall, this research improves the understanding of the parameter sensitivity and performance sensitivity of freight demand model systems to alternative spatial representations of an establishment's relative location. The research findings strongly encourage analysts to acknowledge that the results of freight travel analyses with spatial indicators are sensitive to the definition of areal units.

City Logistics in historic centers: Multi-Criteria Evaluation in GIS for city of Salvador (Bahia – Brazil)

The City Logistic brings a new concept in land use planning and management of urban logistics flows, resolving problems related to urban goods movement, while seeking a balance between the efficiency required by urban freight and the social costs involved (traffic congestion product, environmental impacts and energy conservation). This article seeks to investigate the relationship between urban freight and shape, design, zoning and urban planning to establish City Logistic strategies that support land use planning and organization of logistics flows in the city center of Salvador, Bahia, Brazil. The methodology is mainly based on the use of geoprocessing techniques which are used do diagnose the spatial concentration of Freight Trip Generation Centers in terms of the potentially produced travel in Salvador’s central area. Furthermore, the methodology used the Multi-Criteria Evaluation (MCE) in GIS (Geographic Information System) for identifying suitability maps related to urban logistics activities and land use compatibility. The latter served as a basis to identify strategies and measures of City Logistic to support the improvement of urban logistics in the study area.

Potential last-mile impacts of crowdshipping services: a simulation-based evaluation

Crowdsourced delivery services (crowdshipping) represent a shipping alternative to traditional delivery systems, particularly suitable for e-commerce. Although some benefits in terms of reduced pollution and congestion could be obtained by replacing dedicated freight trips, the impacts of crowdshipping are unclear and depend on several factors such as the transport mode used, the match between supply and demand, length of detours, and possible induced demand. For example, private drivers could modify their existing routes or engage in new trips to pick up and drop off packages; similarly, public transport users could carry along packages on their trips and drop them off at lockers installed around the stations. In this paper, we analyze by means of a simulation-based approach the potential impacts of alternative implementation frameworks. In order to account more realistically for last-mile delivery operations, a hybrid dynamic traffic simulation is adopted such that the macroscopic features of traffic (triggering of congestion, queue spillbacks and interactions with traffic signals) are reproduced in combination with the microscopic features of delivery operations (delivery vehicles are tracked along their routes). The effects on traffic and emissions are investigated for the adoption of crowdshipping by carriers delivering parcels in the city center of Rome, Italy. Results show that not only is the mode employed by crowdshippers crucial for the sustainability of such a measure, but also operational aspects involving the length of detour, parking behavior, and daily traffic variations. Crowdsourced deliveries by car have generally higher negative impacts than corresponding deliveries by public transit. However, limiting the deviations of crowdshippers from the original trips, providing adequate parking options, and incentivizing off-peak deliveries, could significantly reduce crowdshipping externalities.