Yunteng Lao

Vehicle Routing Problem: Simultaneous Deliveries and Pickups with Split Loads and Time Windows

The vehicle routing problem with simultaneous deliveries and pickups (VRPSDP) has attracted much research interest because of the potential to provide cost savings to transportation and logistics operators. Several extensions of VRPSDP exist. Of these extensions, the simultaneous deliveries and pickups with split loads problem (SDPSLP) has been proposed to eliminate vehicle capacity constraints, as well as allow the deliveries or pickups for a customer to be split into multiple visits. Although delivery and pickup activities are often constrained by time windows, few studies have considered such constraints when SDPSLP has been addressed. To fill the gap, this paper formulates the vehicle routing problem of simultaneous deliveries and pickups with split loads and time windows (VRPSDPSLTW) as a mixed-integer programming problem. A hybrid heuristic algorithm was developed to solve this problem. Solomon data sets with minor modifications were applied to test the effectiveness of the solution algorithm. The results of a computational experiment demonstrated that use of the proposed algorithms to solve VRPSDPSLTW had advantages for minimization of the total travel cost, number of vehicles, and loading rate. The proposed formulation and solution algorithm for VRPSDPSLTW may serve as a general analytical tool for the optimization of vehicle routing in practice.

Generalized nonlinear models for rear-end crash risk analysis.

A generalized nonlinear model (GNM)-based approach for modeling highway rear-end crash risk is formulated using Washington State traffic safety data. Previous studies majorly focused on causal factor identification and crash risk modeling using Generalized linear Models (GLMs), such as Poisson regression, Logistic regression, etc. However, their basic assumption of a generalized linear relationship between the dependent variable (for example, crash rate) and independent variables (for example, contribute factors to crashes) established via a link function can be often violated in reality. Consequently, the GLM-based modeling results could provide biased findings and conclusions. In this research, a GNM-based approach is developed to utilize a nonlinear regression function to better elaborate non-monotonic relationships between the independent and dependent variables using the rear end accident data collected from 10 highway routes from 2002 through 2006. The results show for example that truck percentage and grade have a parabolic impact: they increase crash risks initially, but decrease them after the certain thresholds. Such non-monotonic relationships cannot be captured by regular GLMs which further demonstrate the flexibility of GNM-based approaches in the nonlinear relationship among data and providing more reasonable explanations. The superior GNM-based model interpretations help better understand the parabolic impacts of some specific contributing factors for selecting and evaluating rear-end crash safety improvement plans.

Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression.

Two types of animal-vehicle collision (AVC) data are commonly adopted for AVC-related risk analysis research: reported AVC data and carcass removal data. One issue with these two data sets is that they were found to have significant discrepancies by previous studies. In order to model these two types of data together and provide a better understanding of highway AVCs, this study adopts a diagonal inflated bivariate Poisson regression method, an inflated version of bivariate Poisson regression model, to fit the reported AVC and carcass removal data sets collected in Washington State during 2002-2006. The diagonal inflated bivariate Poisson model not only can model paired data with correlation, but also handle under- or over-dispersed data sets as well. Compared with three other types of models, double Poisson, bivariate Poisson, and zero-inflated double Poisson, the diagonal inflated bivariate Poisson model demonstrates its capability of fitting two data sets with remarkable overlapping portions resulting from the same stochastic process. Therefore, the diagonal inflated bivariate Poisson model provides researchers a new approach to investigating AVCs from a different perspective involving the three distribution parameters (λ(1), λ(2) and λ(3)). The modeling results show the impacts of traffic elements, geometric design and geographic characteristics on the occurrences of both reported AVC and carcass removal data. It is found that the increase of some associated factors, such as speed limit, annual average daily traffic, and shoulder width, will increase the numbers of reported AVCs and carcass removals. Conversely, the presence of some geometric factors, such as rolling and mountainous terrain, will decrease the number of reported AVCs.

Gaussian Mixture Model-Based Speed Estimation and Vehicle Classification Using Single-Loop Measurements

Traffic speed and length-based vehicle classification data are critical inputs for traffic operations, pavement design and maintenance, and transportation planning. However, they cannot be measured directly by single-loop detectors, the most widely deployed type of traffic sensor in the existing roadway infrastructure. In this study, a Gaussian mixture model (GMM)-based approach is developed to estimate more accurate traffic speeds and classified vehicle volumes using single-loop outputs. The estimation procedure consists of multiple iterations of parameter correction and validation. After the GMM is established to empirically model vehicle on-times measured by single-loop detectors, the optimal solution can be initially sought to separate length-based vehicle volume data. Based on the on-time of the separated short vehicles from the GMM, an iterative process will be conducted to improve traffic speed and classified volume estimation until the estimation results become statistically stable and converge. This method is straightforward and computationally efficient. The effectiveness of the proposed approach was examined using data collected from several loop stations on Interstate 90 in the Seattle area. The traffic volume data for three vehicle classes are categorized based on the proposed method. The test results show the proposed GMM approach outperforms the previous models, including conventional constant g-factor method, sequence method, and moving median method, and produces more reliable, accurate estimates of traffic speeds and classified vehicle volumes under various traffic conditions.

Short-Term Traffic Flow Forecasting for Freeway Incident-Induced Delay Estimation

Freeway incidents not only threaten travelers’ safety but also cause severe congestion. Incident-induced delay (IID) refers to the extra travel delay resulting from incidents on top of the recurrent congestion. Quantifying IID would help people better understand the real cost of incidents, maximize the benefit-cost ratio of investment on incident remedy actions, and develop active traffic management and integrated corridor management strategies. By combining a modified queuing diagram and short-term traffic flow forecasting techniques, this study proposes an approach to estimate the temporal IID for a roadway section, given that the incidents occurs between two traffic flow detectors. The approach separates IID from the total travel delay, estimates IID for each individual incident, and only takes volume as input for IID quantification, avoiding using speed data that are widely involved in previous algorithms yet are less available or prone to poor data quality. Therefore, this approach can be easily deployed to broader ranges where only volume data are available. To verify its estimation accuracy, this study captures two incident videos and extracts ground-truth IID data, which is rarely done by previous studies. The verification shows that the IID estimation errors of the proposed approach are within 6% for both cases. The approach has been implemented in a Web-based system, which enables quick, convenient, and reliable freeway IID estimation in the Puget Sound region in the state of Washington.

A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

The vehicle routing problem (VRP) is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups (VRPSPDP) is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.

Fuzzy Logic–Based Mapping Algorithm for Improving Animal-Vehicle Collision Data

AbstractAnimal-vehicle collisions (AVCs) cause hundreds of human and wildlife animal fatalities and tens of thousands of human and wildlife animal injuries in North America. It is estimated that AVCs cause more than

Detection and Correction of Inductive Loop Detector Sensitivity Errors by Using Gaussian Mixture Models

1 billion in property damage each year in the United States. Further research efforts are needed to identify effective countermeasures against AVCs. Two types of data have been widely used in AVC-related research: collision reported (CRpt) data and carcass removal (CR) data. However, previous studies showed that these two data set are significantly different, implying the incompleteness in either set of the data. Hence, this study aims at developing an algorithm to combine these two types of data to improve the completeness of data for AVC studies. A fuzzy logic–based data mapping algorithm is proposed to identify matching data from the two data sets so that data are not overcounted when combining the two data sets. The membership functions of the fuzzy logic algorithm are determined by a sur...

Modeling Traffic Flow Dynamics on Managed Lane Facility: Approach Based on Cell Transmission Model

Inductive loop detectors (ILDs) form the backbone of many traffic detection networks by providing vehicle detection for freeway and arterial monitoring as well as signal control. Unfortunately, ILD technology generally has limited the available sensitivity settings. Changing roadway conditions and aging equipment can cause ILD settings that had been correct to become under- or oversensitive. ILDs with incorrect sensitivities may result in severe errors in occupancy and volume measurements. Therefore, sensitivity error identification and correction are important for quality data collection from ILDs. In this study, the Gaussian mixture model (GMM) is used to identify ILDs with sensitivity problems. If the sensitivity problem is correctible at the software level, a correction factor is then calculated for the occupancy measurements of the ILD. The correction methodology developed in this study was found effective in correcting occupancy errors caused by the ILD sensitivity problems. Single-loop speed calculation with the corrected occupancy increases the accuracy by 12%. Since this GMM-based approach does not require hardware changes, it is cost-effective and has great potential for easy improvement of archived loop data quality.

Enabling Network-Wide Tolling-Strategy Assessment via Customized Route-Choice Simulation Module

With the increasing attention paid to environmental effects and sustainable infrastructure, transportation agencies are now seeking more cost-effective and environmentally friendly countermeasures against traffic congestion than the traditional roadway expansion. Managed lane (ML) systems, as an innovative strategy for managing the roadway conditions in real time, have been gaining increasing popularity in the recent decade. However, the unique characteristics of ML facilities, such as the frictional effects between general purpose lanes (GPLs) and their adjacent ML, are not well studied and modeled. This paper investigates the interaction between GPLs and MLs, as buffer-separated ML facilities are readily affected by congestion in the adjacent GPLs. The frictional effect is quantified through development of speed–flow curves for the ML facility. A traffic flow model is developed on the basis of the cell transmission model incorporating this frictional effect to model the traffic evolution on the ML facility. This model is capable of replicating the traffic flow pattern, including congestion onset, propagation, and dissipation at a macroscopic level. The model also captures the effect of GPL congestion on the ML. This model can serve as the underlying ML traffic flow model in future research for evaluating the effectiveness of different tolling strategies.