Simultaneous Route Research Articles

Dynamic System Optimum Analysis of Multi-Region Macroscopic Fundamental Diagram Systems With State-Dependent Time-Varying Delays

This paper investigates the dynamic system optimum (DSO) problem with simultaneous route and departure time assignments for a general traffic network partitioned into multiple regions. Regional traffic congestion is modeled with a well-defined macroscopic fundamental diagram (MFD) mapping the trip completion rate to the vehicular accumulation. To overcome the limitation of inconsistent flow propagation between region boundaries and the corresponding travel time, the state-dependent regional travel time function is explicitly incorporated in the flow propagation of the conventional MFD dynamics. From a systems perspective, the traffic dynamics within a region can be regarded as a dynamic system with an endogenous time-varying delay depending on the system state. Equilibrium condition for the DSO problem is analytically derived through the lens of Pontryagin minimum principle and is compared against the static SO counterpart. The structure of path specific marginal cost is analyzed regarding the path travel cost and early-late penalty function. In contrast to existing analytical methods, the proposed method is applicable for general MFD systems without linearization of the MFD dynamics. Neither approximation of the equilibrium solution nor constant regional delay assumption is required. Numerical examples are conducted to illustrate the characteristics of DSO traffic equilibrium and the corresponding marginal cost together with other dynamic external costs.

A dynamic user equilibrium model for multi-region macroscopic fundamental diagram systems with time-varying delays

Macroscopic fundamental diagram (MFD) has been widely used for aggregate modeling of urban traffic network dynamics to tackle the dimensionality problem of microscopic approaches. This paper contributes to the state-of-the-art by proposing a dynamic user equilibrium (DUE) model that enables simultaneous route choice and departure time choice under the MFD framework for various applications such as park-and-ride, vehicle dispatching and relocation. To better capture the traffic flow propagation and to adapt to the fast time-varying demand, the state-dependent travel time function is integrated into the MFD dynamics as an endogenous time-varying delay. The multi-region MFD dynamics with saturated state and inflow constraints is then used as the network loading model to formulate the DUE model through the lens of the differential variational inequality. Necessary conditions for the DUE are analytically derived using the Pontryagin minimum principle. Difficulties raised in handling the dynamic state-dependent nonlinear travel time functions, state and inflow constraints are addressed without model linearization nor enforcing constant delay assumption as conventionally done in the literature. The additional cost induced by inflow capacity and accumulation constraints can capture the hypercongestion represented by the downward sloping part of the MFD without actually activating traffic congestion. Numerical examples solved by using time-discretization solution algorithm illustrate the DUE characteristics and the corresponding dynamic external costs induced by constraints.

Simultaneous Intramuscular And Intranasal Administration Of Chitosan Nanoparticles-Adjuvanted Chlamydia Vaccine Elicits Elevated Protective Responses In The Lung.

BackgroundChlamydia psittaci is a zoonotic bacteria closely associated with psittacosis/ornithosis. Vaccination has been recognized as the best way to inhibit the spread of C. psittaci due to the majority ignored of infections. The optimal Chlamydia vaccine was obstructed by the defect of single immunization route and the lack of availability of nontoxic and valid adjuvants.MethodsIn this study, we developed a novel immunization strategy, simultaneous (SIM) intramuscular (IM) and intranasal (IN) administration of a C. psittaci antigens (Ags) adjuvanted with chitosan nanoparticles (CNPs). And SIM-CNPs-Ags were used to determine the different types of immune response and the protective role in vivo.ResultsCNPs-Ags with zeta-potential values of 13.12 mV and of 276.1 nm showed excellent stability and optimal size for crossing the mucosal barrier with high 71.7% encapsulation efficiency. SIM-CPN-Ags mediated stronger humoral and mucosal responses by producing meaningfully high levels of IgG and secretory IgA (sIgA) antibodies. The SIM route also led to Ags-specific T-cell responses and increased IFN-γ, IL-2, TNF-α and IL-17A in the splenocyte supernatants. Following respiratory infection with C. psittaci, we found that SIM immunization remarkably reduced bacterial load and the degree of inflammation in the infected lungs and made for a lower level of IFN-γ, TNF-α and IL-6. Furthermore, SIM vaccination with CNPs-Ags had obviously inhibited C. psittaci disseminating to various organs in vivo.ConclusionSIM immunization with CNPs-adjuvanted C. psittaci Ags may present a novel strategy for the development of a vaccine against the C. psittaci infection.

Computing Dynamic User Equilibria on Large-Scale Networks with Software Implementation

Dynamic user equilibrium (DUE) is the most widely studied form of dynamic traffic assignment (DTA), in which road travelers engage in a non-cooperative Nash-like game with departure time and route choices. DUE models describe and predict the time-varying traffic flows on a network consistent with traffic flow theory and travel behavior. This paper documents theoretical and numerical advances in synthesizing traffic flow theory and DUE modeling, by presenting a holistic computational theory of DUE, which is numerically implemented in a MATLAB package. In particular, the dynamic network loading (DNL) sub-problem is formulated as a system of differential algebraic equations based on the Lighthill-Whitham-Richards fluid dynamic model, which captures the formation, propagation and dissipation of physical queues as well as vehicle spillback on networks. Then, the fixed-point algorithm is employed to solve the DUE problems with simultaneous route and departure time choices on several large-scale networks. We make openly available the MATLAB package, which can be used to solve DUE problems on user-defined networks, aiming to not only facilitate benchmarking a wide range of DUE algorithms and solutions, but also offer researchers a platform to further develop their own models and applications. The MATLAB package and computational examples are available at https://github.com/DrKeHan/DTA.

Dynamic system optimum simultaneous route and departure time choice problems: Intersection-movement-based formulations and comparisons

In this paper, we propose intersection-movement-based models to formulate dynamic system optimum simultaneous route and departure time choice (DSO-SRDTC) problems over general networks and compare the models with the link-based and path-based counterparts. Each of these three types of models has four variants, which are formed by whether to consider first-in-first-out (FIFO) constraints and non-vehicle holding (NVH) constraints. In all three types of DSO-SRDTC models and their variants, the link transmission model (LTM) is incorporated as their traffic flow model. The DSO-SRDTC problems without FIFO constraints are formulated as linear programming (LP) problems, while the DSO-SRDTC problems with FIFO constraints are formulated as non-convex non-linear programming problems. We find that existing link-based NVH constraints cannot completely eliminate vehicle holding (VH) solutions, and propose both intersection-movement-based and path-based NVH constraints, which can completely eliminate VH solutions. We also prove that the link-based, intersection-movement-based, and path-based models of DSO-SRDTC problems without FIFO constraints are equivalent in terms of obtaining the same optimal total system travel cost (TSTC). However, the three types of models for DSO-SRDTC problems with FIFO constraints can obtain different optimal TSTCs. Based on the solution properties of the DSO-SRDTC problems with FIFO constraints, branch-and-bound algorithms are modified to solve the DSO-SRDTC problems with FIFO constraints for global optima. Numerical examples are set up to demonstrate the properties and performance of the proposed models. To the best of our knowledge, we are the pioneers to provide intersection-movement-based formulations for DSO-SRDTC problems and analyze their mathematical properties.

Dynamic traffic assignment in degradable networks: paradoxes and formulations with stochastic link transmission model

ABSTRACTThis paper proposes a simultaneous route and departure time choice (SRDTC) problem with fixed demand in a degradable transport network. In this network, travelers face with stochastic travel times. Their selection of routes and departure times follows the UE principle in terms of the mean generalized route cost, which is defined as the probabilistic dynamic user optimal (PDUO) principle. The proposed PDUO-SRDTC problem is formulated as a variational inequality (VI) problem. As a special case of PDUO-SRDTC problem, the PDUO route choice (PDUO-RC) problem is also proposed and formulated as a VI problem. Network degradation is defined on the degradation of the outflow capacity of each link. A Monte Carlo-based stochastic link transmission model (MC-SLTM) is developed to capture the effect of physical queues and the random evolution of traffic states during flow propagation to estimate mean generalized route costs. Both the extragradient algorithm and the route-swapping method with a variable sample size scheme are developed to solve the proposed VI problems. Numerical examples are developed to illustrate the paradoxical phenomena of the problems and the effectiveness of the solution methods. Numerical results show that constructing a new road, enhancing link outflow capacity, or reducing outflow capacity degradation can lead to poor network performance and it is important to consider both network degradation and queue spillback when making transportation policies aimed at improving network performance. The results also demonstrate that the variable sample size scheme can give a quicker and better solution than the traditional fixed sample size scheme.

AND SOLUTION METHOD TO A SIMULTANEOUS ROUTE DESIGN AND FREQUENCY SETTING PROBLEM FOR A BUS RAPID TRANSIT SYSTEM IN COLOMBIA

ABSTRACT We propose a model and solution method to a simultaneous route design and frequency setting problem on a main corridor from one of the Bus Rapid Transit (BRT) Systems of Colombia. The proposed model considers objectives of users and operators in a combinatorial multi-objective optimization framework and takes into account real constraints on the operation of some Colombian BRT systems not found in previous models. The problem is solved heuristically by a Genetic Algorithm which is tailored from an existing work, to consider specific characteristics of the real scenario. The methodology is validated with current data from one of the most important bus corridors in a Colombian BRT system. The results obtained improve the current solutions for this corridor.

Multiclass, simultaneous route and departure time choice dynamic traffic assignment with an embedded spatial queuing model

ABSTRACTThis paper develops a complementarity formulation for a multi-user class, simultaneous route and departure time choice dynamic user equilibrium (DUE) model. A path-based multiclass cell transmission model (mCTM) is embedded to propagate the traffic flow on the network. Heterogeneous user classes are incorporated in the new formulation and heterogeneity is based on different preferred arrival times, cost perception for travel time, early and late arrival penalties. Multiple model properties have been showed. The proposed model is solved as an equivalent non-monotone variational inequality (VI) problem defined on a product set. A modified proximal point algorithm is used to solve the proposed non-monotone VI problem. Numerical results show that the solution approach is able to find the equilibrium or close to equilibrium solutions. The new formulation and solution approach show the feasibility of solving the multiclass DUE problem for general traffic networks.

The nonlinear equation system approach to solving dynamic user optimal simultaneous route and departure time choice problems

Dynamic user optimal simultaneous route and departure time choice (DUO-SRDTC) problems are usually formulated as variational inequality (VI) problems whose solution algorithms generally require continuous and monotone route travel cost functions to guarantee convergence. However, the monotonicity of the route travel cost functions cannot be ensured even if the route travel time functions are monotone. In contrast to traditional formulations, this paper formulates a DUO-SRDTC problem (that can have fixed or elastic demand) as a system of nonlinear equations. The system of nonlinear equations is a function of generalized origin-destination (OD) travel costs rather than route flows and includes a dynamic user optimal (DUO) route choice subproblem with perfectly elastic demand and a quadratic programming (QP) subproblem under certain assumptions. This study also proposes a solution method based on the backtracking inexact Broyden–Fletcher–Goldfarb–Shanno (BFGS) method, the extragradient algorithm, and the Frank-Wolfe algorithm. The BFGS method, the extragradient algorithm, and the Frank-Wolfe algorithm are used to solve the system of nonlinear equations, the DUO route choice subproblem, and the QP subproblem, respectively. The proposed formulation and solution method can avoid the requirement of monotonicity of the route travel cost functions to obtain a convergent solution and provide a new approach with which to solve DUO-SRDTC problems. Finally, numeric examples are used to demonstrate the performance of the proposed solution method.

Solving simultaneous route guidance and traffic signal optimization problem using space-phase-time hypernetwork

This paper addresses the problem of simultaneous route guidance and traffic signal optimization problem (RGTSO) where each vehicle in a traffic network is guided on a path and the traffic signals servicing these vehicles are set to minimize their travel times. The network is modeled as a space-phase-time (SPT) hyper-network to explicitly represent the traffic signal control phases and time-dependent vehicle paths. A Lagrangian-relaxation-based optimization framework is proposed to decouple the RGTSO problem into two subproblems: the Route Guidance (RG) problem for multiple vehicles with given origins and destinations and the Traffic Signal Optimization (TSO) problem. In the RG subproblem, the route of each vehicle is provided subject to time-dependent link capacities imposed by the solution of the TSO problem, while the traffic signal timings are optimized according to the respective link travel demands aggregated from the vehicle trajectories. The dual prices of the RG subproblem indicate search directions for optimization of the traffic signal phase sequences and durations in the TSO subproblem. Both RG and TSO subproblems can be solved using a computationally efficient finite-horizon dynamic programming framework, enhanced by parallel computing techniques. Two numerical experiments demonstrated that the system optimum of the RGTSO problem can be quickly reached with relatively small duality gap for medium-size urban networks.

A nonlinear equation system approach to the dynamic stochastic user equilibrium simultaneous route and departure time choice problem

In dynamic stochastic user equilibrium simultaneous route and departure time choice (DSUE-SRDTC) problems, route travel costs can be non-monotone even if route travel times are monotone with respect to route flows. As a result, the mapping function of the variational inequality (VI) problems for the DSUE-SRDTC problems can be non-monotone, and many existing solution algorithms developed for the DSUE-SRDTC problems do not guarantee convergence under this non-monotone condition. This paper formulates the DSUE-SRDTC problem with fixed demand as a system of nonlinear equations. The mapping function of the proposed system of nonlinear equations is defined by a dynamic route choice problem, which can also be formulated as a VI problem with a strictly monotone mapping function under some assumptions. This property enables that the solution algorithm for the DSUE-SRDTC problem can avoid the requirement of the monotonicity of the route travel cost functions for the convergence of the solution procedure. A backtracking inexact Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is adopted to solve the system of nonlinear equations, and iterative methods are developed to generate an initial solution for the BFGS method and solve the dynamic route choice problem. Finally, numerical examples are set up to show that the proposed method outperforms many existing algorithms for solving the DSUE-SRDTC problem in terms of guaranteeing solution convergence.

Investigating undesired spatial and temporal boundary effects of congestion charging

Two types of reported problems are related to the existing congestion charging projects that levy traffic only in a certain area within one or a few time periods during the day. One is that travellers depart earlier or later than a charging period to avoid paying full or part of the congestion charging tolls, which creates two undesired demand peaks that are often greater than available capacity. One peak comes just before the start of congestion charging and the other follows the end of it. We term this phenomenon ‘temporal boundary effect’ of congestion charging. The other reported problem is that travellers would rather stay away from a charging zone than pay congestion charging tolls, which causes undesired congestion on those roads or paths on the edge of the charging zone. We call this phenomenon ‘spatial boundary effect’ generated by congestion charging. This research investigates these boundary effects in the context of simultaneous route and departure time choice dynamic user equilibrium (SRD-DUE) network flows with an aim to gain new insights into congestion charging design. Numerical experiments investigating constant and time-varying congestion charging toll profiles are presented in this paper. This investigation shows that congestion charging may not be able to eliminate hypercongestion efficiently if schemes are not well designed, and can unfortunately give rise to undesired boundary effects and that a simply designed congestion charging scheme with small level toll or time-varying toll profiles can reduce the magnitude of boundary effects but may not be able to fully eliminate such undesired effects.

Optimization and Stabilization of Electrodeposited Cu2ZnSnS4 Photocathodes for Solar Water Reduction

Cu2ZnSnS4 (CZTS) is a promising p-type semiconductor that has not yet been extensively investigated for solar fuel production via water splitting. Here, we optimize and compare two different electrodeposition routes (simultaneous and sequential) for preparing CZTS electrodes. More consistent results are observed with the simultaneous route. In addition, the effect of etching and the presence of a CdS buffer layer on the photocurrent are investigated. Finally, we demonstrate for the first time the stabilization of these electrodes using protecting overlayers deposited by atomic layer deposition (ALD). Our best performing protected electrodes (Mo/CZTS/CdS/AZO/TiO2/Pt) exhibited a photocurrent of over 1 mA cm(-2) under standard one sun illumination conditions and a significant improvement in stability over unprotected electrodes.

Stochastic Choice Behavior on Road Traffic Networks under Information Provision

This paper attempts to model stochastic choice behavior in simultaneous trip route and departure time decision-making on road traffic networks, taking into account information quality and individual differences in information interpretation among the population of travelers. Different from the traditional stochastic model, the proposed choice behavior model assumes that road users simultaneously select the trip routes and departure times that have the largest probabilities of incurring the least generalized travel costs. This model is applicable in both static and dynamic settings and can be applied to both ordinary travelers as well as operators of emergent vehicles, e.g., the fire engine. The preliminary numerical experiments show that the proposed stochastic choice model can reflect the overreaction phenomena reported in studies of traffic information provision and the impacts of the types of traffic information on the effectiveness of information provision. This model opens a potential way to analyze network equilibrium behavior taking into account individual differences in the ability of information interpretation as well as information quality.

Existence of simultaneous route and departure choice dynamic user equilibrium

This paper is concerned with the existence of the simultaneous route-and-departure choice dynamic user equilibrium (SRDC-DUE) in continuous time, first formulated as an infinite-dimensional variational inequality in Friesz et al. (1993). In deriving our existence result, we employ the generalized Vickrey model (GVM) introduced in Han et al. (2013a,b) to formulate the underlying network loading problem. As we explain, the GVM corresponds to a path delay operator that is provably strongly continuous on the Hilbert space of interest. Finally, we provide the desired SRDC-DUE existence result for general constraints relating path flows to a table of fixed trip volumes without invocation of a priori bounds on the path flows.

Effects of queue spillover in networks considering simultaneous departure time and route choices

This paper explores the effects of queue spillover in transportation networks, in the context of dynamic traffic assignment. A model of spatial queue is defined to characterize dynamic traffic flow and queuing formation in network links. Network users simultaneously choose departure time and travel route to minimize the travel cost including journey time and unpunctuality penalty. Using some necessary conditions of the dynamic user equilibrium, dynamic network flows are obtained exactly on some networks with typical structure. Various effects of queue spillover are discussed based on the results of these networks, and some new paradoxes of link capacity expansion have been found as a result of such effects. Analytical and exact results in these typical networks show that ignoring queuing length may generate biased solutions, and the link storage capacity is a very important factor concerning the performance of networks.

Engineered polyethylenimine/graphene oxide nanocomposite for nuclear localized gene delivery

Nuclear localized signals (NLS) capable of recognition by nuclear transport proteins can aid the nuclear translocation of the payloads. In this report, NLS peptide PKKKRKV (PV7, one of the primary NLS peptides) was introduced into GO-PEI (10 kDa)/DNA binary complexes to engineer a nuclei localized gene delivery system, based on electrostatic and hydrogen-bonded interactions. To assess the functional mechanism of NLS peptides in the GO-PEI system, the PV7 was introduced via three different routes, including post-addition after the formation of a GO-PEI/DNA binary, simultaneous addition with GO-PEI, pDNA, and prior-addition into the cell culture before treatment with GO-PEI/DNA complexes. In vitro transfection investigations revealed that the ternary composites engineered by the simultaneous route (that is the second route referred to above) exhibit a higher transfection efficiency in comparison with GO-PEI 10 kDa or PEI 10 kDa and are even comparable with PEI 25 kDa with optimized parameters. The study on the intracellular uptake of Cy3 labeled pDNA indicated that the addition of PV7 could effectively assist the GO-PEI to deliver plasmid DNA directly into the nucleus without obvious aggregations. With the improved capability of gene delivery, however, the cytotoxicity of GO-PEI was much lower than PEI 10 kDa and PEI 25 kDa against both HeLa cells and 293 T cells. Therefore, the PV7 conjugated GO-PEI system compromised the contradiction between the cytotoxicity and transfection efficiency, which could be an alternative strategy for a nuclear targeted gene delivery vehicle.

Modeling of Effects of Peak Load Pricing on Metropolitan Network and Activities

Peak load pricing has long been seen as a way to internalize externalities, as a set of incentives to shift peak hour trips to off-peak periods, and as a mechanism to generate revenues. However, how travelers trade time for money and respond to peak–off-peak pricing differentials is an open question that generates some timely and related questions, including the following: (a) How can the activity location and traffic implications for multiple times of the day in a major metropolitan area be modeled? (b) What are the network effects of peak load pricing on selected routes on level of service and urban development? It is possible to conduct simulations on actual highway networks to treat these questions. None of the urban models, however, can examine simultaneous route choice and time-of-day choice involving millions of travelers, thousands of traffic network zones, and hundreds of thousands of network links in an equilibrium system. This research addressed these questions by extending the Southern California Planning Model so that it could be used to determine the effects of pricing schemes on the time-of-day choice for travel, trip distribution, and network traffic for the greater Los Angeles, California (five-county), metropolitan area. The model estimated improvements in levels of service throughout the highway network for alternative toll charges. The model examined how drivers traded route choice with time-of-day choice against the option of traveling less. The approach also estimated the revenues implied for local jurisdictions as well as the possible effects of altered development pressures on land use throughout the region. The effects of two different toll scenarios were compared, and policy implications were offered.

Morning commute problem considering route choice, user heterogeneity and alternative system optima

This paper extends the bottleneck model to study congestion behavior of morning commute and its implications to transportation economics. The proposed model considers simultaneous route and departure time choices of heterogenous users who are distinguished by their valuation of travel time and punctual arrival. Moreover, two dynamic system optima are considered: one minimizes system cost in the unit of monetary value (i.e., the conventional system optimum, or SO) and the other minimizes system cost in the unit of travel time (i.e., the time-based SO, or TSO). Analytical solutions of no-toll equilibrium, SO and TSO are provided and the welfare effects of the corresponding dynamic congestion pricing options are examined, with and without route choice. The analyses suggest that TSO provides a Pareto-improving solution to the social inequity issue associated with SO. Although a TSO toll is generally discriminatory, anonymous TSO tolls do exist under certain circumstances. Unlike in the case with homogenous users, an SO toll generally alters users’ route choices by tolling the poorer users off the more desirable road, which worsens social inequity. Numerical examples are presented to verify analytical results.

ChemInform Abstract: High-Temperature Oxidation Mechanisms of m- and p-Xylene

Mechanisms for the oxidation of m- and p-xylene were developed with the aid of atmospheric flow reactor tests at temperatures ranging from 1093 to 1199 K. The experimental data suggested that m-xylene is oxidized by sequential oxidation and removal of the methyl side chains while p-xylene reacts through simultaneous, as well as sequential, oxidation of the side chains. The formation of p-xylylene (3,6-bis(methylene)-1,4-cyclohexadiene), postulated to occur during the oxidation of p-xylene, opens up the simultaneous oxidation route and leads to the formation of p-phthalaldehyde