Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Transportation Networks. Optimality. Cost Functions. Deterministic User Equilibrium Assignment. Stochastic User Equilibrium Assignment. Trip Table Estimation. Network Reliability. Network Design. Conclusions. References. Index.

Transportation network analysis

This history of African slavery from the fifteenth to the early twentieth centuries examines how indigenous African slavery developed within an international context. Paul E. Lovejoy discusses the medieval Islamic slave trade and the Atlantic trade as well as the enslavement process and the marketing of slaves. He considers the impact of European abolition and assesses slavery's role in African history. The book corrects the accepted interpretation that African slavery was mild and resulted in the slaves' assimilation. Instead, slaves were used extensively in production, although the exploitation methods and the relationships to world markets differed from those in the Americas. Nevertheless, slavery in Africa, like slavery in the Americas, developed from its position on the periphery of capitalist Europe. This new edition revises all statistical material on the slave trade demography and incorporates recent research and an updated bibliography.

P. E. Lovejoy, Transformations in Slavery: A History of Slavery in Africa

This paper deals with route choice models capturing travelers' strategic behavior when adapting to revealed traffic conditions en route in a stochastic network. The strategic adaptive behavior is conceptualized as a routing policy, defined as a decision rule that maps from all possible revealed traffic conditions to the choices of next link out of decision nodes, given information access assumptions. In this paper, we use a specialized example where a variable message sign provides information about congestion status on outgoing links. We view the problem as choice under risk and present a routing policy choice model based on the cumulative prospect theory (CPT), where utility functions are nonlinear in probabilities and thus flexible attitudes toward risk can be captured. In order to illustrate the differences between routing policy and non-adaptive path choice models as well as differences between models based on expected utility (EU) theory and CPT, we estimate models based on synthetic data and compare them in terms of prediction results. There are large differences in path share predictions and the results demonstrate the flexibility of the CPT model to represent varying degrees of risk aversion and risk seeking depending on the outcome probabilities.

Adaptive route choices in risky traffic networks: A prospect theory approach

This paper develops a novel linear programming formulation for autonomous intersection control (LPAIC) accounting for traffic dynamics within a connected vehicle environment. Firstly, a lane based bi-level optimization model is introduced to propagate traffic flows in the network, accounting for dynamic departure time, dynamic route choice, and autonomous intersection control in the context of system optimum network model. Then the bi-level optimization model is transformed to the linear programming formulation by relaxing the nonlinear constraints with a set of linear inequalities. One special feature of the LPAIC formulation is that the entries of the constraint matrix has only {−1, 0, 1} values. Moreover, it is proved that the constraint matrix is totally unimodular, the optimal solution exists and contains only integer values. It is also shown that the traffic flows from different lanes pass through the conflict points of the intersection safely and there are no holding flows in the solution. Three numerical case studies are conducted to demonstrate the properties and effectiveness of the LPAIC formulation to solve autonomous intersection control.

A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment

The ability to make optimal transportation network investments decision is central to the strategic management of transportation systems. The presence of uncertainty in transportation systems presents new challenges in making optimal network investment decisions. In this paper, the authors develop a multi time period network design problem considering both demand uncertainty and demand elasticity. Such an approach affords the planner the flexibility to delay, change, or even abandon the future network investment. The authors measure the flexibility of investing over multiple time periods as compared to a single-stage network design decision. Initially, the authors provide a taxonomy and define many dimensions of transportation network flexibility. This is followed with the development of a flexible network design formulation (FNDP), in which the investment is staged over multiple time periods. The demand is assumed to be separable and the demand elasticity is captured using a negative exponential distribution. The authors develop the FNDP formulation as bilevel stochastic mathematical programming with complementarity constraints (STOCH-MPEC) in which the bi-level formulation is converted to a single level using non-linear complementarity constraint conditions for user equilibrium (UE) problem. The formulation is implemented on two test networks and the results show the benefits of FNDP over single-stage NDP--measured in terms of increase in present expected system consumer surplus (PESCS)--are in the range of 10-30%. The results clearly demonstrate that under demand uncertainty there are potential benefits of introducing flexibility in investment decisions. Finally, the authors conduct a sensitivity analysis of FNDP with different budget values and it is observed that certain paradoxical sharp corners are observed at certain budget values.

Multi-period transportation network design under demand uncertainty

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...

Observed Trip Chain Behavior of Commercial Vehicles

Pedestrian temporal and spatial gap acceptance at mid-block street crossing in developing world

A robust transportation signal control problem accounting for traffic dynamics

This paper introduces a multi-commodity, single (generic) vehicle formulation of freight ODS model that combines a commodity-based model to estimate loaded truck trips and a complementary model of empty trips. This integration is important because explicit modeling of empty trips—that account for 30% to 40% of total truck trips—is required to avoid significant errors in the estimation of the directional traffic. The formulation is then applied to a case study. Two cases of the proposed model are studied. The first one uses total traffic in the estimation; while the second one is based on loaded and empty traffic. The results conclusively show that the models that consider an empty trip submodel significantly outperform the model that does not in their ability to replicate the observed traffic counts. The comparison between the results from the multi-commodity ODS and the single commodity ODS previously developed by the authors indicates that the multi-commodity formulation brings about substantial reductions in the error associated with the estimation of observed traffic counts. These reductions, in the order of 20% for empty traffic and 40% for loaded and total traffic, seem larger than the spurious improvement to be expected from the increased number of parameters, suggesting that the multi-commodity ODS formulation performs better. The results also show some minor improvements in the ability of the multi-commodity ODS formulation to estimate the OD matrices. In terms of the model's ability to correctly estimate the “true” value of the parameters of the models used, i.e., the parameter values estimated by calibrating the model directly from the OD data, it was found that the multicommodity ODS procedure is able to provide fairly good estimates Noortman and van Es's model parameters, though the parameters of the gravity models that came out to be quite different than the “true” values. The overall assessment of the formulation introduced here is that it represents a solid improvement with respect to comparable techniques.

Gopal R. Patil

Papers

Multi-period transportation network design under demand uncertainty

Observed Trip Chain Behavior of Commercial Vehicles

Pedestrian temporal and spatial gap acceptance at mid-block street crossing in developing world

A robust transportation signal control problem accounting for traffic dynamics

A Multicommodity Integrated Freight Origin–destination Synthesis Model