Total Network Travel Time Research Articles

Mitigation strategy of cascading failures in urban traffic congestion based on complex networks

Urban road traffic network is becoming increasingly complex. The increasing travel demand has exceeded facility supply of transportation system. Thus, a scientific way of transportation can solve the problem of traffic congestion. If some central intersections are congested, the road network could easily lose its travel efficiency and further lead to cascading failure. In this regard, from the viewpoint of complex networks, this paper provides a cascading failure mitigation strategy for the urban road traffic network. A load redistribution strategy is represented which can redistribute congestion load reasonably. Random and intentional failures are simulated with MATLAB on Barabási and Albert (BA) scale-free network. The indices related to complex network of different methods are compared on BA network and part of the real road network in Baoding City. The load redistribution strategy in this paper is implemented combined with user equilibrium assignment, then the travel time related to urban road network is compared with another method on the Sioux Falls network. The simulation results suggest that when the capacity parameter reaches the threshold, further increase has little effect on the mitigation time. Comparisons of other mitigation approaches verify the effectiveness of our approach in lessening cascading failure caused by traffic congestion. The increase in total network travel time and additional delay by reason of load redistribution is acceptable.

Demonstrative Case of a Pedestrian Network Design Model Considering Effects on Motorized Traffic

The transformation of urban roadways into pedestrian streets is a popular measure for reshaping city parts and enhancing their livability. Nevertheless, pedestrianization schemes are expected to have some impact on the performance of the neighboring road network, especially if these are established ad-hoc or solely based on non-transport criteria. This study introduces a methodological tool for supporting decisions on implementing pedestrianization schemes in urban networks. A bi-level network design model variant is developed for that purpose, whose design objective is to maximize the extent of pedestrian streets in an urban network, while maintaining acceptable impacts to the performance of the road network. Alternative decisions on pedestrianization are considered for each network segment; these include partial (one-directional) or complete (bi-directional) pedestrianization under physical and operational criteria and constraints. The model is applied for a mid-sized urban network in Greece and solved using a genetic algorithm. Results show that the pedestrianization of almost 7% of the road network in relation to length leads to a 40% increase in total network travel time, while a higher ratio of complete versus partial pedestrianization is more advantageous. Outcomes also reveal that that rigid design guidelines should be examined in a case-by-case approach, as superior results may be attained if some constraints, such as those related to the overall street width, are relaxed. Reasonably, policy priorities significantly impact generated solutions and are expected to play a decisive role in the design of pedestrianization schemes.

Hierarchical ramp metering in freeways: An aggregated modeling and control approach

This paper develops a model-based hierarchical control method for coordinated ramp metering on freeway networks with multiple bottlenecks and on- and off-ramps. The controller consists of two levels where at the upper level, a Model Predictive Control (MPC) approach is developed to optimize total network travel time by manipulating total inflow from on-ramps to the freeway network. The lower level controller distributes the optimal total inflows to each on-ramp of the freeway based on local traffic state feedback. The control method is based on a parsimonious aggregated traffic model that relates the freeway total outflow to the number of vehicles on the freeway sections.Studies on aggregated traffic modeling of networks have shown the existence of a well-defined and low-scatter Macroscopic Fundamental Diagram (MFD) for urban networks. The MFD links network aggregated flow and density (accumulation). However, the MFD of freeway networks typically exhibits high scatter and hysteresis loops that challenge the control performance of MFD-based controllers for freeways. This paper addresses these challenges by modelling the effect of density heterogeneity along the freeway and capacity drop on characteristics of freeway MFD using field traffic data. In addition, we introduce a model to predict the evolution of density heterogeneity that is essential to reproduce the dynamics of freeway MFD accurately. The proposed model is integrated as the prediction model of the MPC in the hierarchical control method.The proposed coordinated ramp metering method shows desirable performance to reduce the vehicles total time spent and eliminate congestion. The control approach is compared with other coordinated ramp metering controllers based on the MPC framework with different traffic prediction models (e.g.CTM and METANET). The outcomes of numerical experiments highlight that the MFD-based hierarchical controller (i) is better able to overcome the modeling mismatch between the prediction model and the plant (process model) in the MPC framework and (ii) requires less computation effort than other nonlinear controllers.

The structure of public-private sector collaboration in travel information markets: A game theoretic analysis

In recent years there has been substantial growth in the prevalence of ad-hoc data exchange arrangements between local traffic authorities and commercial traffic information service providers. Although these arrangements are widely regarded as mutually beneficial, in fact to date, no comprehensive analysis exists of the operation of this information market, nor of its consequences for the different market participants involved. To address this gap, this paper presents a new framework which enables the analysis of the long-term outcomes of various collaboration schemes for traffic information service providers, local traffic authorities and network users. The framework is based on a bi-level non-cooperative Nash game in which the upper level represents the data exchange arrangements between local traffic authorities and commercial traffic information service providers and the lower level represents the impact of the provided information services on network users. The framework is flexible and can accommodate a variety of different market structures and commercial behaviours. The game theoretic model is formulated and solved as an equivalent equilibrium problem with equilibrium constraints. Numerical experiments are undertaken using this framework to explore the consequence of a number of commonly observed real-world data exchange arrangements. This analysis leads to three general conclusions. First, the results suggest that when a local traffic authority seeks only to minimise the total network travel time and offers free collaboration schemes, it should collaborate with all the cooperating service providers in the market. Second, if conversely, a local traffic authority seeks only to maximise its revenue from selling its data to service providers, it should be aware that its revenue does not always increase by selling the data to more service providers, since the willingness of service providers to pay for data declines as more providers are granted access. And finally, if a local traffic authority seeks to establish paid schemes that balance the benefit to network users and its own revenue benefits, then circumstances can easily arise in which these two objectives are in conflict with one another.

Evaluation of a Proposed Road in a Campus Network based on Ideal Flow

A proposed road project inside a campus that will have to down trees from its mini forest have attracted different public opinion among the faculty and students. In this paper, we would like to justify our view objectively based on transportation engineering point of view. The Ideal Flow Network (IFN) method was used to do the analysis because its source code is publicly available for clarification. The network data is based on previous study of Ateneo Traffic Group report. Two scenarios were set: based scenario that represents the current road network, and two proposed scenario that represents the current road network with additional proposed road in two ways and one way respectively. Analysis of the results show that the total network travel time of the proposed scenario are increased by 4.69% and 2.32% respectively for two ways and one-way scenarios. The network speed will be slightly improved by 0.03% in when the proposed road project is added in two ways. Thus, we failed to justify that the proposed network has better network performance.

A Multi-objective Network Design Model for Post-disaster Transportation Network Management

Despite their inherent vulnerability to structural and functional degradation, transportation networks play a vital role in the aftermath of disasters by ensuring physical access to the affected communities and providing services according to the generated needs. In this setting of operational conditions and service needs which deviate from normal, a restructuring of network functions is deemed to be beneficial for overall network serviceability. In such context, this paper explores the planning of post-disaster operations on a network following a hazardous event on one of the network’s nodes. Lane reversal, demand regulation and path activation are applied to provide an optimally reconfigured network with reallocated demand, so that the network performance is maximized. The problem is formulated as a bi-level optimization model; the upper level determines the optimal network management strategy implementation scheme while the lower level assigns traffic on the network. Three performance indices are used for that purpose: the total network travel time (TNTT), the total network flow (TNF) and the special origin-destination pair (OD pair) accessibility. A genetic algorithm coupled with a traffic assignment process is used as a solution methodology. Application of the model on a real urban network proves the computational efficiency of the algorithm; the model systematically produces robust results of enhanced network performance, indicating its value as an operation planning tool.

A complementarity equilibrium model for electric vehicles with charging

Abstract This paper presents a complementarity equilibrium model for electric vehicles (EVs). Under the equilibrium conditions, each EV takes the path that is shortest and does not violate the driving range. When the driving range has to be violated, the EVs are allowed to choose a path with a charging station to extend their driving range. To find the shortest such path, a constrained shortest path problem with replenishment (CSPP with replenishment) is formulated that considers the driving range limit of EVs. The CSPP is solved with a label-correcting algorithm with two additional steps that substantially reduce the computation time and the required memory. The first procedure is a pruning technique that eliminates exploring branches (of an enumeration tree) that can no longer become incumbent and the second procedure is an indexing technique that works as a pointer for navigating the generated (enumeration) tree when it becomes too large. Numerical experiments on a number of networks show a substantially lower computation time compared to existing algorithms and the results provide several insights into the driving patterns of EVs. When charging time is increased, the EVs shift to paths that have a longer travel time but a shorter distance. Hence, the total network distance decreases but the total network travel time increases. We also show that unregulated expansion of the charging infrastructure can actually increase the total network travel time due to the presence of Braess’ paradox.

Bounding the inefficiency of the C-logit stochastic user equilibrium assignment

Compared with standard logit-based stochastic user equilibrium assignment model, the C-logit model describes route choice behavior in a more realistic way by considering the overlapping effect between routes. This paper investigates the inefficiency upper bounds of this model against the deterministic system optimum and the C-logit stochastic system optimum in terms of the total network travel time. It is found that the commonality factor of overlapping routes significantly affects the inefficiency bound, besides link congestion degree, total demand and the number of feasible routes. If the commonality factor is not considered, the efficiency loss resulting from selfishly stochastic travel behavior will be to large extent underestimated.

Estimation of travel time reliability in large-scale networks

Over the last two decades, travel time reliability has been increasingly used as a key performance indicator of transportation networks. Central to the assessment of travel time reliability is the estimation of the probability mass function (PMF) of total network travel time. This paper aims to present an efficient method to estimate the PMF of total network travel time using the universal generating function (UGF) method. Moreover, the paper proposes two truncation techniques to increase the computational efficiency of the UGF method. In order to assess the applicability of the method in practice, the method is tested on different networks. The results suggest that the method is computationally much more efficient than the standard crude Monte Carlo simulation technique at different confidence levels, and that it can be applied to real-world cases with a reasonable computation time.

Joint Evacuation and Emergency Traffic Management Model with Consideration of Emergency Response Needs

During and after a disaster, transportation network management is a complex task. In a state of limited serviceability and functionality, surviving transportation infrastructure is expected to act as a vital lifeline and to support different types of operations. This study investigates the joint planning of evacuation operations and emergency traffic management while accounting for emergency response needs. Extended wildfires across a highway network are the assumed disaster setting. Population evacuation takes place by using part of the network, whereas emergency traffic management strategies are applied outside the evacuation zone. Network performance enhancement is pursued through the implementation of lane reversal. The problem is formulated as a two-stage optimization model; minimization of total evacuation time (TET) and minimization of total network travel time (TNTT) are the upper-level objectives for the evacuation area and the rest of the network, respectively. The lower level corresponds to a traffic assignment model on the basis of user equilibrium. Demonstration of the model on a real-world network proves the computational efficiency of the algorithm. The model systematically produces robust results in terms of TET-TNTT minimization and thus addresses the operational needs arising and enhances overall transportation network performance.

Reliability-Based Marginal Cost Pricing Problem Case with Both Demand Uncertainty and Travelers’ Perception Errors

Focusing on the first-best marginal cost pricing (MCP) in a stochastic network with both travel demand uncertainty and stochastic perception errors within the travelers’ route choice decision processes, this paper develops a perceived risk-based stochastic network marginal cost pricing (PRSN-MCP) model. Numerical examples based on an integrated method combining the moment analysis approach, the fitting distribution method, and the reliability measures are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of travel time reliability and travelers’ perception errors may significantly reduce the performance of the first-best MCP tolls, especially under high travelers’ confidence and network congestion levels. The analysis result could also enhance our understanding of (1) the effect of stochastic perception error (SPE) on the perceived travel time distribution and the components of road toll; (2) the effect of road toll on the actual travel time distribution and its reliability measures; (3) the effect of road toll on the total network travel time distribution and its statistics; and (4) the effect of travel demand level and the value of reliability (VoR) level on the components of road toll.

Robust optimization approach for system optimal dynamic traffic assignment with demand uncertainty

This research deals with the path-based system optimal dynamic traffic assignment (SODTA) problem with uncertain demands that are assumed to be bounded by a prescribed uncertainty set. A robust optimization approach is adopted to address this problem. The objective is to minimize the total network travel time under the worst-case scenario defined by a pre-determined demand uncertainty set. We formulate the robust counterpart optimization problem of SODTA for a general uncertainty set and show that solving the robust SODTA problem is not more difficult than solving the deterministic SODTA problem for some specific types of uncertainty set. Moreover, a column generation-based algorithmic framework that embeds a scaled gradient projection algorithm is proposed to solve the SODTA problem. Numerical experiments were conducted to demonstrate the effectiveness of the algorithm and to examine the impact of different types of demand uncertainty set on solution quality.

An integrated scenario tree model for stochastic degradable road network design against recurrent congestions and sporadic disasters

This paper proposes an integrated scenario tree model that incorporates recurrent congestions and sporadic disasters into a stochastic degradable road network design problem (SDNDP). The traffic pattern of the stochastic degradable network (SDN) under the recurrent congestion condition is evaluated by probit-based stochastic user equilibrium (called SDN-SUE), whereas the system optimum is used to assess the traffic pattern of the SDN under the sporadic disaster (called SDN-SO). The proposed model determines optimal link capacity expansions that minimize the sum of the total network travel time costs of all recurrent congestion conditions plus the total expansion cost subject to the desired total network travel time constraints for evacuation purposes and the SDN-SUE and SDN-SO conditions. A solution algorithm is also developed for solving the SDNDP. Numerical examples are given to demonstrate the potential pitfall in considering the network improvement policies separately and to show the benefit from the integrated model.

Approximate analytical expressions for transportation network performance under demand uncertainty

This paper deals with accounting for demand uncertainty in the symmetric traffic equilibrium problem. We initially show that by not accounting for long term demand uncertainty, the total network travel time obtained from the traffic equilibrium problem is substantially underestimated. Then, we develop closed form approximate analytical expressions for expected value and the variance of the network performance (a measure of total system travel time) resulting from uncertain demand. We empirically show the relationship between the uncertain OD demand and the link/path flows in a network with the assumption that the long term demand is normally distributed. Illustrative numerical experiments are conducted on multiple test networks and the total network impact in terms of total system travel time is compared with the derived analytical expressions. It is observed that the expressions for the expected value match very closely to the true system impact for small networks and becomes an approximation as the network size grows. The robustness (variance) expression is observed to be more deviant for larger networks than the expected value. However, the ordinal ranking of robustness was found to be consistent with the true network robustness. Further, recent work on OD correlations, applicability on large networks and directions for future research are discussed. The analytical expressions should allow the characterization of network uncertainty when link flow uncertainty is known.

Critical Link Identification for Traffic Incident Management Programme under Degradable Stochastic Network

This paper proposed a new index called traffic incident management ratio (TIMR) for identifying appropriate roads to be installed with traffic incident management system. The TIMR is defined as the ratio between the expected total travel costs of the degradable transport network without and with traffic incident management programme (TIMP). The traffic flow pattern under the degradation network without TIMP is assumed to follow User Equilibrium (UE) condition, whereas the system optimum (SO) assignment is used to characterise the degradable network condition controlled by TIMP. The main function of TIMP considered in this paper is re-routing of traffic after the incident. An optimisation programme is then proposed for identifying critical roads in the network for an installation of TIMP. The critical roads for TIMP is identified by maximising the TIMR which implies that incidents on these critical roads can significantly increase the total network travel time without TIMP in which the installation of TIMP...

IMPACT OF ROAD PRICING ON THE NETWORK RELIABILITY

In this paper, the impact of road pricing on the transport network reliability is investigated with the use of a newly developed reliability-based user equilibrium (RUE) model. The RUE model can be adopted to overcome the difficulty in predicting the flow pattern in a congested and unreliable road network. In this RUE model, both the travel time and travel time reliability are taken into account for drivers' route choices by introducing a concept of path preference index (PI) to quantify the attractiveness of each alternative path for travel between a particular origin-destination pair. The PI is a weight sum of the path travel time index (TI) and the path travel time reliability index (RI), which represent the travel time and travel time reliability on a particular path respectively. The RUE model can be applied to investigate the effects of road pricing on travel time and reliability in urban road networks, in which drivers would consider both the travel time and its reliability for their route choices. In this paper, the RUE model is extended to maximize the network reliability by road pricing. The results demonstrated the trade-off between the total network travel time and the network travel time reliability. The effects of the toll on the total network travel time and the network travel time reliability are investigated.

Mixed Travel Behavior in Networks with ATIS and Upper Bound of Efficiency Loss

Abstract In a transportation network supported by the Advanced Traveler Information Systems (ATIS), the drivers equipped with ATIS are directed by a control center to select their routes so as to minimize the total network travel time, while the unequipped ones make the route choice decisions on the base of minimizing their individual travel times. This article presents a variational inequality model to formulate the mixed travel behavior associated with both system optimum and user equilibrium, and derives a general formula for the upper bound of efficiency loss of the mixed behavior. The upper bounds when all link travel cost functions are polynomial are examined against different ATIS market penetrations. It is found that the upper bound is related to the ATIS market penetration and the corresponding mixed equilibrium flow pattern.

Improvement and Evaluation of Cell-Transmission Model for Operational Analysis of Traffic Networks

The cell-transmission model (CTM), developed by Daganzo in 1994, has not been fully exploited as an operations model for analysis of large-scale traffic networks. Because of its macroscopic and mesoscopic features, CTM offers calibration and computational advantages over microscopic models. This paper demonstrates specific improvements to the original form of CTM to increase its accuracy and realism of traffic flow representation. These improvements include modifications to provide flexibility in selection of cell lengths, noninteger movements of vehicles between cells, and algorithmic enhancements in the merging and diverging logics. The effect of such improvements on the performance of CTM was evaluated independently and comparatively. A sample freeway network of the I-10 corridor in Baton Rouge, Louisiana, was used to evaluate and compare the performance of the improved version of CTM versus CORSIM under heavily congested traffic conditions. The results showed comparable performance of both platforms in terms of link occupancy (density) and total network travel time.

Modelling network travel time reliability under stochastic demand

A technique is proposed for estimating the probability distribution of total network travel time, in the light of normal day-to-day variations in the travel demand matrix over a road traffic network. A solution method is proposed, based on a single run of a standard traffic assignment model, which operates in two stages. In stage one, moments of the total travel time distribution are computed by an analytic method, based on the multivariate moments of the link flow vector. In stage two, a flexible family of density functions is fitted to these moments. It is discussed how the resulting distribution may in practice be used to characterise unreliability. Illustrative numerical tests are reported on a simple network, where the method is seen to provide a means for identifying sensitive or vulnerable links, and for examining the impact on network reliability of changes to link capacities. Computational considerations for large networks, and directions for further research, are discussed.

Scheduling of Lane Closures Using Genetic Algorithms with Traffic Assignments and Distributed Simulations

In past research, several versions of hybrid genetic algorithm-simulation methodology have been proposed for scheduling of multiple lane closures that aims to minimize a network's total traffic delay. The genetic algorithm is used as a search engine for generation of lane closure schedule, while a microscopic traffic simulation model is employed to calculate the total network travel time under each lane closure scenario. A difficulty in implementing this methodology practically is the long computing time required, due to the many simulation runs needed to evaluate the average total network travel time of each feasible schedule. This paper applies the precondition technique, standard error criterion, and termination criterion to reduce the number of necessary simulation runs. As a further improvement, traffic simulations are distributed in different processors of a multiprocessor machine. To further reduce the computing time, a two-stage hybrid genetic algorithm methodology has been proposed in this paper. This two-stage methodology consists of a hybrid genetic algorithm-traffic assignment methodology as the first stage, followed by a hybrid genetic algorithm-distributed simulation methodology as the second stage. The traffic assignment model is used to replace the traffic simulation model in the estimation of total network travel time in stage 1. The applications of the improvement techniques have been demonstrated through a hypothetical problem involving 20 lane closure requests in a network consisting of 986 links, 397 nodes, and 22 origin-destination zones. Together, these improvement techniques contributed to up to 87% reduction in waiting time for a solution of the example problem.