Gopal R. Patil

Observed Trip Chain Behavior of Commercial Vehicles

Results are presented from a comprehensive analysis of the observed trip chain behavior of commercial vehicles in the Denver, Colorado, region on the basis of data collected by the Denver Regional Council of Governments. These analyses may provide the foundation for further research into commercial vehicle trip chain models. Trip chain behavior is characterized by number of trip chains, length of trip chains, and conditional probability of trip purpose. For this analysis, trip purposes were grouped as freight transportation; transport of people; service calls; fuel, service, and so forth; and return to base or home. Commercial vehicles were grouped as automobile, pickup van, single-unit truck, combination truck, and sports utility vehicle. The conditional probabilities were analyzed for a fixed length of trip chain and were conditioned on stop number. Although most vehicles were found to make one trip chain per day, one of four commercial vehicles made more than one trip chain per day. The analyses also i...

A robust transportation signal control problem accounting for traffic dynamics

Transportation system analysis must rely on predictions of the future that, by their very nature, contain substantial uncertainty. Future demand, demographics, and network capacities are only a few of the parameters that must be accounted for in both the planning and every day operations of transportation networks. While many repercussions of uncertainty exist, a primary concern in traffic operations is to develop efficient traffic signal designs that satisfy certain measures of short term future system performance while accounting for the different possible realizations of traffic state. As a result,uncertainty has to be incorporated in the design of traffic signal systems. Current dynamic traffic equilibrium models accounting for signal design, however, are not suitable for quantifying network performance over the range of possible scenarios and in analyzing the robust performance of the system. The purpose of this paper is to propose a new approach-robust system optimal signal control model; a supply-side within day operational transportation model where future transportation demand is assumed to be uncertain. A robust dynamic system optimal model with an embedded cell transmission model is formulated. Numerical analysis are performed on a test network to illustrate the benefits of accounting for uncertainty and robustness.

System-Optimal Stochastic Transportation Network Design

In transportation engineering, the network design problem (NDP) aims at finding an optimal link improvement in a network for given demand. Although traffic demand is essentially uncertain, many previous studies assumed it to be deterministic. The demand stochasticity is incorporated and formulas are developed for system-optimal (SO) flow stochastic NDP. The SO assumption is justified by comparing results from SO deterministic NDP with those of user-equilibrium NDP. The difference in social cost between the two approaches is found to be less than 5%. Two two-stage stochastic programs with recourse formulations are proposed: one with penalty function and the other without. The main advantage of the first formulation is that a planner can exert better control on improvement by appropriately weighing reduction in the congestion versus improvement costs. The challenge, however, lies in selecting an appropriate penalty function. A nonlinear penalty function is found suitable for the test network studied. The second formulation does not require penalty function but results in a large number of scenarios. Nonanticipativity constraints are introduced in the second formulation to arrive at uniform improvement over all scenarios. Both formulations are solved on a test network. It is found that necessary improvements and the total costs with both models are more than those for average demand.

Integrated Origin–Destination Synthesis Model for Freight with Commodity-Based and Empty Trip Models

Origin–destination (O-D) synthesis techniques offer the potential of producing estimates of freight O-D matrices by using secondary sources such as traffic counts, bypassing the need for expensive data collection efforts. In the case described in this paper, this promise is particularly important because of the significant cost associated with conducting surveys to obtain O-D patterns of freight movements and because of the reluctance of freight providers to provide information they consider commercially sensitive. The paper proposes an integrated O-D synthesis model that combines a commodity-based model to estimate loaded truck trips with a complementary model of empty trips. This integration is important because explicit modeling of empty trips—which account for 30% to 40% of total truck trips—is required to avoid significant errors in the estimation of directional traffic. The proposed model is applied to a case study for which both the actual O-D matrix and traffic counts are known. Two objective functions are considered for the proposed model based on two scenarios: (a) only total link traffic is known and (b) only the split of loaded and empty link traffic is known. With data from a case study, it was found that the proposed model performs significantly better than an alternative formulation that does not consider an empty trip model. In addition, it is observed that the added information in terms of observed empty trips improves the O-D estimates. The model was found to produce reasonable estimates of the true parameters of the underlying models.

Temporal and Spatial Gap Acceptance for Minor Road at Uncontrolled Intersections in India

Capacity analysis of unsignalized intersections is done primarily with gap acceptance principles. The vehicles on lower-priority approaches maneuver when a suitable gap is available in higher-priority conflicting streams. Although temporal gaps are widely used, some researchers advocate the use of spatial gaps. The focus of this study was on analyzing temporal and spatial gaps at four-legged, partially controlled intersections in India. Unlike in developed countries, unsignalized intersections in India are not controlled with stop and yield signs with explicit priorities. The priorities are mainly set by the situations drivers perceive. Field data were collected at three four-legged intersections with video cameras. Temporal and spatial critical gaps were estimated with Raffs, logit, lag, Ashworths, and maximum likelihood methods. The values of temporal critical gap by different methods were found to vary between 3.0 and 3.9 s. The spatial critical gap values varied from 29 to 36 m. These values were smaller than the similar values reported in developed countries, indicating aggressiveness in Indian drivers. The insights from this study can be used for the capacity analysis of unsignalized intersections in India.

Exploring User Behavior In Online Network Equilibrium Problems

The availability of advanced traveler information systems technologies has provided opportunities for travelers to obtain online travel information and thereby improve their travel experiences. The resulting change in user behavior because of such information needs to be accounted for in traffic assignment models. A methodology that accounts for user recourse in the traffic assignment problem is proposed. This methodology accounts for the heterogeneity in user information perception by using a sequential form of logit model. Hyperpaths instead of elementary paths are used in this approach. A key difficulty in modeling online recourse in network assignment problems is in obtaining efficient hyperpaths. A methodology that uses a sequential form of logit model that obviates explicit enumeration of hyperpaths is proposed. Computational results from a hypothetical transportation network are presented to demonstrate the application of the proposed assignment approach.

Sample Average Approximation Technique for Flexible Network Design Problem

Finding an optimal investment strategy to use scarce resources efficiently is challenging, since the transportation network parameters such as demand, capacity, and travel cost are uncertain. Sequencing investments over time can give flexibility to the planner so as to change, delay, or even abandon the future investment based on system realization. This paper presents a stochastic mathematical program with equilibrium constraints (STOCH-MPEC) formulation for a multistage network design problem, flexible network design problem (FNDP), accounting for demand stochasticity and demand elasticity. STOCH-MPEC problems can be computationally intractable, if the number of scenarios is large and/or the study network is large-scale. To reduce the associated complexity of FNDP, we develop a sample average approximate method (SAA) to efficiently solve the flexible network design problem. We implement the SAA on a test network and compare the performance of SAA with different sample sizes. We show that SAA can produce solutions that are close to the true solutions with considerably fewer scenarios and hence can be a viable computational technique for the stochastic network design problem.

Capacity uncertainty on urban road networks: A critical state and its applicability in resilience quantification

There are many aspects of urban transportation that represent sources of uncertainty in the design of roadways, such as the level of capacity needed to ensure efficient traffic flow. As a result of uncertainty in roadway capacity, an urban road network can be deemed to operate at different capacity levels. Some of these levels will have unused capacity, whereas some others will not be enough to cater traffic from all origins to all destinations. Past models assume knowledge over the pattern of these uncertainties. However, it is difficult to gather such knowledge from field observations, and it is absent for majority of the worlds urban areas. We present an alternative methodology in which the capacities are considered as variables that can take any value from zero to a practically realizable maximum. Using a minimax optimization formulation, we determine bounds on urban roadway capacity levels, below which the traffic demand will go unmet. We call this the critical state, and define it as a state of link capacities which effects in the maximum irreducible operational cost on the network with the demand getting fulfilled. We prove that at a critical state, the total travel time (or cost) of the system will be a unique value; i.e. for a given urban road network and a given traffic demand, there is an associated unique critical travel time. We illustrate that this unique travel time—which is an aggregate value of the travel times from all roads on the network—can be used as a benchmark to create various metrics for the urban road network. As an illustrative example on the applicability of critical state, we compare the unique travel time with the best possible travel time on the network, and develop a metric for network resilience. Network resilience is calculated as a normalized difference of the critical and best operation costs. Two-space genetic algorithm is used to solve the problem formulation. The formulation and the solution methodology are illustrated on test networks and results are presented.

Quantifying resilience using a unique critical cost on road networks subject to recurring capacity disruptions

This paper presents a methodology to quantify resilience of transportation networks that are subject to recurring capacity disruptions. System-optimal total travel time at full-capacities is usually adopted as a performance-benchmark on networks. Capacity degradation results in different capacity combinations, and thus, there can be different travel times. We thus compare the best network performance with an upper bound of network performance—indicating how much disruptions the network can take in before it displaces from a demand-meeting state to a demand-not-meeting state—and construct an index of network resilience. For this, we establish a critical state which is an upper bound of network cost under recurring capacity degradation. We define discrete capacity levels and search for probability values over those levels that would result in a critical state. We formulate the critical state link disruption problem as a minimax optimisation problem, where expected system travel time is maximised with respect to probability of recurrence and minimised with respect to link flow. We prove that the network cost is unique at the critical state, although the critical degradation need not be. We solve the minimax problem using a coevolutionary algorithm. We exemplify the formulation on test networks and quantify the improvement in network resilience by retrofitting the Sioux Falls network.

Classification of Gaps at Uncontrolled Intersections and Midblock Crossings Using Support Vector Machines

Gap acceptance predictions provide very important inputs for performance evaluation and safety analysis of uncontrolled intersections and pedestrian midblock crossings. The focus of this paper is on the application of support vector machines (SVMs) in understanding and classifying gaps at these facilities. The SVMs are supervised learning techniques originating from statistical learning theory and are widely used for classification and regression. In this paper, the feasibility of the SVM in analyzing gap acceptance is examined by comparing its results with existing statistical methods. To accomplish that objective, SVM and binary logit models (BLMs) were developed and compared by using data collected at three types of uncontrolled intersections. SVM performance was found to be comparable with that of the BLM in all cases and better in a few. Also, the categorical statistics and skill scores used for validating gap acceptance data revealed that the SVM performed reasonably well. Thus, the SVM technique can be used to classify and predict accepted and rejected gap values according to speed and distance of oncoming vehicles. This technique can be used in advance safety warning systems for vehicles and pedestrians waiting to cross major stream vehicles.