Storage space allocation in container terminals

Container terminals are essential intermodal interfaces in the global transportation network. Efficient container handling at terminals is important in reducing transportation costs and keeping shipping schedules. In this paper, we study the storage space allocation problem in the storage yards of terminals. This problem is related to all the resources in terminal operations, including quay cranes, yard cranes, storage space, and internal trucks. We solve the problem using a rolling-horizon approach. For each planning horizon, the problem is decomposed into two levels and each level is formulated as a mathematical programming model. At the first level, the total number of containers to be placed in each storage block in each time period of the planning horizon is set to balance two types of workloads among blocks. The second level determines the number of containers associated with each vessel that constitutes the total number of containers in each block in each period, in order to minimize the total distance to transport the containers between their storage blocks and the vessel berthing locations. Numerical runs show that with short computation time the method significantly reduces the workload imbalance in the yard, avoiding possible bottlenecks in terminal operations.     

A cross-country study of fairness and infringement on freedom as determinants of car owners' acceptance of road pricing

Jakobsson et al. (2000) found that in Sweden public acceptance of road pricing decreases if it is perceived to be unfair and to infringe on freedom. The present study reports a survey investigating whether the same effects are found in the Asian countries of Japan and Taiwan. The results indicate that fairness plays the same role. However, income had a direct effect on acceptance in Taiwan but not in Japan or Sweden.     

Correlation between automotive CO, HC, NO, and PM emission factors from on-road remote sensing: implications for inspection and maintenance programs

Carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NO) emission factors (EFs) are measured with a commercial vehicle emissions remote sensing system (VERSS) during a large-scale vehicle exhaust emissions study in Las Vegas. Particulate matter (PM) EFs are simultaneously measured for individual vehicles with a newly developed PM-VERSS based on ultraviolet backscatter light detection and ranging (Lidar). The effectiveness of CO and HC EFs as proxy for NO and PM EFs for spark-ignition vehicles is evaluated. Poor correlations were found between EFs for pollutants on an individual vehicle basis indicating that high EFs for one or more pollutants cannot be used as a predictor of high EFs for other pollutants. Stronger functional relationships became evident after averaging the EF data in bins based on rank-order of a single pollutant EF. Low overlap between the highest 10% emitters for CO, HC, NO, and PM was found. These results imply that for an effective reduction of the four pollutants, inspection and maintenance (I/M) programs, including clean screening, should measure all four pollutants individually. Fleet average CO and HC concentrations determined by gaseous VERSS were compared with fleet average CO and HC concentrations measured at low-idle and at high-idle during local I/M tests for spark-ignition vehicles. The fleet average CO concentrations measured by I/M tests at either idle were about half of those measured by remote sensing. The fleet average high-idle HC concentration measured by I/M tests was about half of that measured by VERSS while low-idle I/M and VERSS HC average concentrations were in better agreement. For a typical vehicle trip, most of the fuel is burned during non-idle conditions. I/M measurements collected during idling conditions may not be a good indicator of a vehicle’s potential to be a high emitter. VERSS measurements, when the vehicle is under a load, should more effectively identify high emitting vehicles that have a large contribution to the mobile emissions inventory.     

Hub location with flow economies of scale

A characteristic feature of hub and spoke networks is the bundling of flows on the interhub links. This agglomeration of flows leads to reduced travel costs across the interhub links. Current models of hub location do not adequately model the scale economies of flow that accrue due to the agglomeration of flows. This paper shows that current hub location models, by assuming flow-independent costs, not only miscalculate total network cost, but may also erroneously select optimal hub locations and allocations. The model presented in this paper more explicitly models the scale economies that are generated on the interhub links and in doing so provides a more reliable model representation of the reality of hub and spoke networks.     

An Optimal Self-Tuning Controller for an Active Suspension

An optimal self-tuning control algorithm is presented for vehicle suspension design. The controller, incorporating a weighting controller, state observer and parameter estimator, is designed according to linear optimal control (LQG) theory. Based on the updated estimates of vehicle parameters and states, and the adapted weighting parameters, the LQG controller provides the optimal set of gains over different operating conditions. The feasibility and effectiveness of the proposed self-tuning system was investigated and proved by simulation studies.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.     

A Simplified Model for the Effect of Weld-Induced Residual Stresses on the Axial Ultimate Strength of Stiffened Plates

The present work investigates the compressive axial ultimate strength of fillet-welded steel-plated ship structures subjected to uniaxial compression, in which the residual stresses in the welded plates are calculated by a thermo-elasto-plastic finite element analysis that is used to fit an idealized model of residual stress distribution. The numerical results of ultimate strength based on the simplified model of residual stress show good agreement with those of various methods including the International Association of Classification Societies (IACS) Common Structural Rules (CSR), leading to the conclusion that the simplified model can be effectively used to represent the distribution of residual stresses in steel-plated structures in a wide range of engineering applications. It is concluded that the widths of the tension zones in the welded plates have a quasi-linear behavior with respect to the plate slenderness. The effect of residual stress on the axial strength of the stiffened plate is analyzed and discussed.     

Analysis of the effectiveness of control measures to mitigate road dust emissions in a regional network

The effectiveness of control measures to reduce road dust emissions is analyzed using a year’s data of road dust emissions collected with a mobile sampling platform and a survey of road maintenance practices in the Lake Tahoe Basin of Nevada and California US. Attributes such as sweeping practices, anti-icing, shoulder improvement, pavement condition, trackout, and abrasive material from road segments were analyzed with a feature subset selection algorithm. Street sweeping was found to be an effective means of controlling dust emissions from roads. Road dust from dirty tertiary roads served as a continuous source of suspendable material for adjacent high-speed roads in the winter time. To be most effective, emission control strategies require that not only primary roads, but all roads be swept after snow storms to recover applied abrasive material.     

Assessing the representativeness of a smartphone-based household travel survey in Dar es Salaam,Tanzania

The household travel survey (HTS) finds itself in the midst of rapid technological change. Traditional methods are increasingly being sidelined by digital devices and computational power—for tracking movements, automatically detecting modes and activities, facilitating data collection, etc.. Smartphones have recently emerged as the latest technological enhancement. FMS is a smartphone-based prompted-recall HTS platform, consisting of an app for sensor data collection, a backend for data processing and inference, and a user interface for verification of inferences (e.g., modes, activities, times, etc.). FMS, has been deployed in several cities of the global north, including Singapore. This paper assesses the first use of FMS in a city of the global south, Dar es Salaam. FMS in Dar was implemented over a 1-month period, among 581 adults chosen from 300 randomly selected households. Individuals were provided phones with data plans and the FMS app preloaded. Verification of the collected data occurred every 3 days, via a phone interview. The experiment reveals various social and technical challenges. Models of individual likelihood to participate suggest little bias. Several socioeconomic and demographic characteristics apparently do influence, however, the number of days fully verified per individual. Similar apparent biases emerge when predicting the likelihood of a given day being verified. Some risk of non-random, non-response is, thus, evident.