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This research presents an arrival notification system for paratransit passengers with disabilities. Almost all curb-to-curb paratransit services have a significantly large pickup time window, ranging from 20 to 40 min from the scheduled time and producing substantial passenger waiting times. The arrival notification system presented in this study delivers an automated voice call to a registered user once the paratransit vehicle is in proximity to the pickup location. The system utilizes the Google Traffic application programming interface (API) for the vehicle arrival estimation. Unlike other vehicle arrival notification systems in the state of the practice, the proposed system is compact and does not require additional equipment such as radio transmitting and positioning devices. The proposed system, which uses consumer mobile devices with the Android or iOS platform, is designed to exploit commercial cellular network service (i.e., 3G and 4G-LTE). In addition to the passenger notification, the proposed system provides paratransit drivers with real-time route guidance information developed through the Google Maps API. Field evaluation conducted in Essex County, New Jersey, revealed significant reduction in passenger waiting time. The passenger waiting time was reduced by 15 to 20 min. In addition, the accuracy of the notification system was tested. During the test, in almost all cases, the vehicle arrived 1 min earlier than the proposed arrival time.

Highway lane closures due to road reconstruction and the resulting work zones have been a major source of nonrecurring congestion on freeways. It is extremely important to calculate the safety and cost impacts of work zones: the use of new technologies that track drivers and vehicles make that possible. A multilayer feed-forward artificial neural network (ANN) model is developed in this paper to estimate work zone delay by using the probe-vehicle data. The probe data include the travel speeds under normal and work zone conditions. Unlike previous models, the proposed model estimates temporal and spatial delays, which are applied to a real world case study in New Jersey. The work zone data (i.e., starting time, duration, length, and number of closed lanes) were collected on New Jersey freeways in 2014 together with actual probe-vehicle speeds. A comparative analysis was conducted; the results indicate that the ANN model outperforms the traditional deterministic queuing model in terms of the accuracy in estimating travel delays. The ANN model can be used to calculate contractor penalty in terms of cost overruns as well as incentivize a reward schedule in case of early work competition. The model can assist work zone planners in designing optimal start and end time of work zone as function of time of day. In assessing the performance of work zones, the model can assist transportation engineers to better develop and evaluate traffic mitigation and management plans.

This research evaluates the impact of In-vehicle Signal Advisory System (ITSAS) on signalized arterial. ITSAS provides individual drivers equipped with a mobile communication device with advisory speed information enabling to minimize the time delay and fuel consumption when crossing intersection. Given the instantaneous vehicular driving information, such as position, speed, and acceleration rate, ITSAS produces advisory speed information by taking into consideration the traffic signal changes at a downstream intersection. The advisory speed information includes not only an optimal speed range updated every 300-ft for individual drivers but also a descriptive message to warn drivers stop to ensure safety at the downstream intersection. Unlike other similar Connected Vehicles applications for intersection management, ITSAS does not require Roadside Equipment (RSE) to disseminate the advisory speed information as it is designed to exploit commercial cellular network service (i.e., 3G and 4G-LTE). Thus, ITSAS can be easily plugged into existing traffic control management system to rapidly conduct its implementation without significant additional cost. This research presents the field evaluations of ITSAS on a signalized corridor in New Jersey, which discovered significant travel time savings for the equipped vehicle.

Current incident detection and traffic monitoring method using closed-circuit television (CCTV) cameras meets with limitations as the coverage of CCTV cameras rapidly expands. In general, traffic operators at Traffic Operation Center (TOC) have to manage and monitor numerous CCTV cameras deployed on roadways. Thus, many transportation agencies consider the use of video analytics system to reduce incident detection time and minimize traffic impacts, but they also want to validate the performance of the video analytics system whether it can work with their existing video surveillance infrastructure before procuring the system. To that end, a pilot study was designed and conducted to evaluate the accuracy of a video analytics product by integrating with CCTV cameras deployed on highways. The pilot study was designed to evaluate the accuracy of video analytics in detecting incidents and collecting traffic counts. The test results show that the performance of video analytics is significantly impacted by video quality and other environmental factors such as lighting and weather conditions.

Effective learning and recognition methods play an important role in intelligent image search and video retrieval. This chapter therefore reviews some popular learning and recognition methods that are broadly applied for image search and video retrieval . First some popular deep learning methods are discussed, such as the feedforward deep neural networks , the deep autoencoders , the convolutional neural networks, and the Deep Boltzmann Machine (DBM) . Second, Support Vector Machine (SVM), which is one of the popular machine learning methods, is reviewed. In particular, the linear support vector machine, the soft-margin support vector machine, the non-linear support vector machine , the simplified support vector machine , the efficient Support Vector Machine (eSVM) , and the applications of SVM to image search and video retrieval are discussed. Finally, other popular kernel methods and new similarity measures are briefly reviewed.

The ever-increasing popularity of intelligent image search and video retrieval warrants a comprehensive study of the major feature representation and extraction methods often applied in image search and video retrieval. Towards that end, this chapter reviews some representative feature representation and extraction approaches, such as the Spatial Pyramid Matching (SPM) , the soft assignment coding, the Fisher vector coding , the sparse coding and its variants, the Local Binary Pattern (LBP) , the Feature Local Binary Patterns (FLBP) , the Local Quaternary Patterns (LQP), the Feature Local Quaternary Patterns (FLQP) , the Scale-invariant feature transform (SIFT) , and the SIFT variants, which are broadly applied in intelligent image search and video retrieval .

A hybrid machine-learning model, integrating an artificial neural network (ANN) and a support vector machine (SVM) model, is developed to predict spatiotemporal delays, subject to road geometry, number of lane closures, and work zone duration in different periods of a day and in the days of a week. The model is very user friendly, allowing the least inputs from the users. With that the delays caused by a work zone on any location of a New Jersey freeway can be predicted. To this end, tremendous amounts of data from different sources were collected to establish the relationship between the model inputs and outputs. A comparative analysis was conducted, and results indicate that the proposed model outperforms others in terms of the least root mean square error (RMSE). The proposed hybrid model can be used to calculate contractor penalty in terms of cost overruns as well as incentive reward schedule in case of early work competition. Additionally, it can assist work zone planners in determining the best start and end times of a work zone for developing and evaluating traffic mitigation and management plans.

The second generation of the Freight Analysis Framework, known as FAF2, is a continuation of the original Freight Analysis Framework developed by the U.S. Department of Transportation, Federal Highway Administration. FAF2 provides trip interchanges for commodity flows between 114 domestic zones, 17 additional international gateways at which imports enter and exports depart the United States, and seven international regions. This paper presents methods for disaggregating the FAF2 data to the county level by developing different disaggregation factors for different commodity types. These new methods are also compared with other disaggregation methods. The objective is to enable state and local governmental agencies to utilize FAF2 commodity origin–destination data for a quick desktop analysis and to devise further strategies in collecting and acquiring local commodity data. The focus area of this study is the state of New Jersey. The study developed and applied different methods to disaggregate FAF2 commodity data down to the New Jersey county level. The results of the disaggregation were then compared with Global Insight's Transearch Database and other disaggregation methods previously developed and presented as part of this study. Findings indicate that no one disaggregation method produces the best results for trip productions and attractions. Disaggregating each commodity using commodity-specific industry employment data yielded the best results in matching the Transearch database for flow origins. However, simple non-commodity-specific factors, such as truck vehicle miles traveled, total employment, or adjusted population data, generally yielded better results in disaggregating flow attractions.

Increasing global trade has created the need for efficient container ports. The goal of the port is to move containers as quickly as possible and at the least possible cost. Goods that are delayed at the port are inevitably tardy when delivered to the customer, and thus sanctioned by late charges. Two key activities in the port are (i) unloading of containers from truck and then storage in the export area, and (ii) removal of containers from import storage and then loading onto the trucks. Since containers are large and heavy, specialized material handling vehicles are required for transporting them within the terminal. The focus of this paper is on port terminals where straddle carriers are primarily used to move containers. Container terminals typically have well developed computer and communication networks. Through these networks a terminal scheduler will control and schedule the movement of the straddle carrier fleet in real time. The objective of the terminal scheduler is to minimize the empty trave...

Bus routing is one of the most important elements of public transit system planning. This article presents a model of optimizing service headway and a bus route serving an area with a commuter (many-to-one) travel pattern. The bus route is optimized by minimizing the total system cost, including operator and user costs, while considering diagonal links in the study network. A method is developed for transforming this network into a pure grid, which enables construction of pure grid network models applicable to irregular grid networks. A case is presented to demonstrate the application of the model. Results show that the optimal bus route is sensitive to demand distribution over the service area. The developed model is particularly useful for planning a new bus service and evaluating an existing one in many cities embedded with general grid networks.

Current analytic models for optimizing urban bus transit systems tend to sacrifice geographic realism and detail in order to obtain their solutions. The models presented here shows how an optimization approach can be successful without oversimplifying spatial characteristics and demand patterns of urban areas and how a grid bus transit system in a heterogeneous urban environment with elastic demand is optimized. The demand distribution over the service region is discrete, which can realistically represent geographic variation. Optimal network characteristics (route and station spacings), operating headways and fare are found, which maximize the total operator profit and social welfare. Irregular service regions, many-to-many demand patterns, and vehicle capacity constraints are considered in a sequential optimization process. The numerical results show that at the optima the operator profit and social welfare functions are rather flat with respect to route spacing and headway, thus facilitating the tailoring of design variables to the actual street network and particular operating schedule without a substantial decrease in profit. The sensitivities of the design variables to some important exogenous factors are also presented.

This paper presents the exact formulation and solution of an integrated mode-choice traffic-assignment model. The model incorporates theoretically sound behavioral principles governing mode and route selection. The paper also demonstrates how to implement the model within powerful mathematical programming software and use it to analyze an intermodal transportation network. Results of the analysis show that the model is an excellent educational tool in aiding the understanding of traveler behavioral principles. As a practical tool, the model can be used to provide insights in the analysis of intermodal commuter corridors.

Results of research undertaken to evaluate the educational needs of the emerging field of Intelligent Transportation Systems (ITSs) are presented, and whether course offerings in academic programs meet these needs is ascertained. A survey was conducted to determine needs for ITS education among public- and private-sector entities and to assess the perception of the academic sector on the type and level of ITS education to be offered. The results indicate that academic programs are aware of the educational needs of the private and public sectors in ITS and have been reacting to those needs. A traditional civil engineering curriculum is inadequate to educate engineers in ITS, thus the ITS educational program must be inter-(or cross-) disciplinary. Focus should be on the areas that are not traditionally part of civil engineering education such as communications, traffic surveillance, systems analysis, and social and institutional issues. The results are intended primarily to provide a guideline for universities to develop the content of an ITS program by identifying the type of program structure as well as the topics to be covered in the courses. A case study is presented of how such a program was implemented at the graduate level at the New Jersey Institute of Technology.

This paper has two purposes. One is to discuss and define the concepts of productivity and quality as they apply to transportation, particularly freight transportation, and to identify the similarities and differences with other service industries (e.g., banking and social services). The second purpose is to describe how productivity and quality improvements can be achieved in the trucking (or drayage) portion of intermodal rail-truck service, and the implications of these for market penetration.