Intermodal Path Algorithm for Time-Dependent Auto Network and Scheduled Transit Service

A simple but efficient algorithm is proposed for finding the optimal path in an intermodal urban transportation network. The network is a general transportation network with multiple modes (auto, bus, rail, walk, etc.) divided into the two major categories of private and public, with proper transfer constraints. The goal was to find the optimal path according to the generalized cost, including private-side travel cost, public-side travel cost, and transfer cost. A detailed network model of transfers between modes was used to improve the accounting of travel times during these transfers. The intermodal path algorithm was a sequential application of specific cases of transit and auto shortest paths and resulted in the optimal intermodal path, with the optimal park-and-ride location for transferring from private to public modes. The computational complexity of the algorithm was shown to be a significant improvement over existing algorithms. The algorithm was applied to a real network within a dynamic traffic and transit assignment procedure and integrated with a sequential activity choice model

Approach to Modeling Demand and Supply for a Short-Notice Evacuation

As part of disaster mitigation and evacuation planning, planners must be able to develop effective tactical and operational strategies to manage traffic and transportation needs during an evacuation. One aspect of evacuation planning is the estimation of how many people must be evacuated to provide strategies that are responsive to the number and location of these people. When such estimates are available, it may be possible to implement tactical and operational strategies that closely match the likely demand on the road network during the evacuation. With short notice for an evacuation, people may need to be evacuated directly from current locations. In addition, for some disasters, the spatial extent of the evacuated area may change over time. This problem may be exacerbated by congestion around the evacuated area. An estimation process is proposed for a short-notice evacuation. The method uses on-hand data typically generated through existing travel demand models at many metropolitan planning organizations. It estimates demand using convenient models for trip generation, trip distribution, and travel time generation for these trips, considering a staged evacuation. These demand estimates feed a dynamic simulation model, DynusT, that is used to model the supply characteristics of the roadway network during the evacuation. Such models can be applied using a case study based on a short-notice flooding scenario for Phoenix, Arizona

Modeling Transit and Intermodal Tours in a Dynamic Multimodal Network

A fixed-point formulation and a simulation-based solution method were developed for modeling intermodal passenger tours in a dynamic transportation network. The model proposed in this paper is a combined model of a dynamic traffic assignment, a schedule-based transit assignment, and a park-and-ride choice model, which assigns intermodal demand (i.e., passengers with drive-to-transit mode) to the optimal park-and-ride station. The proposed model accounts for all segments of passenger tours in the passengers' daily travel, incorporates the constraint on returning to the same park-and-ride location in a tour, and models individual passengers at a disaggregate level. The model has been applied in an integrated travel demand model in Sacramento, California, and feedback to the activity-based demand model is provided through separate time-dependent skim tables for auto, transit, and intermodal trips

Capacitated Schedule-Based Transit Assignment Using a Capacity Penalty Cost

Schedule-based transit assignment models have been studied extensively from 2000, considering more time-dependent transit passenger behavior associated with the transit schedule. Currently, transit schedule information is more easily accessed using new telecommunications systems, such as mobile devices and the internet. One critical example of information sharing is Google's General Transit Feed Specification (GTFS). The information of the schedule per se, however, is not enough to explain the transit passenger's behavior, especially in a congested transit system. Regarding the congestion issues on a transit system, numerous researches have studied a transit schedule network (Nguyen et al., 2001; Nuzzolo et al., 2001; Poon et al., 2004; Hamdouch and Lawphonpanich, 2008, 2010).Along the stream toward understanding transit passenger behavior in the capacitated transit schedule network, we propose solution models for solving the deterministic and stochastic user equilibrium (SUE) problems on a capacitated transit schedule network. Nguyen et al. (2001) introduced how the capacitated user equilibrium (UE) on a transit schedule network is different from the auto user equilibrium. For the foundation of the study, we utilize the link-based and time-expanded (LBTE) transit schedule network introduced by Noh et al. (2012a) which effectively captures turning movements like transfers easily as well as maintaining the efficient size of a schedule-based network. In the LBTE transit network, time points are assigned to each link connecting two stops by each run (or route). Utilizing the "link-based" structure, a link-based shortest path (LBSP) and hyperpath search (LBHP) models (Noh et al., 2012a) are introduced. Especially, the hyperpath employs a log-sum weighting function for incorporating multiple schedule alternatives at each stop node considering passenger's stochastic behavior. One distinctive transit passenger behavior over a congested transit system is a first-in-first-out (FIFO) priority on boarding. A passenger already on board has the higher priority than passengers who are about to boarding, and the passengers arriving earlier at a stop will have higher priority than the passengers arriving later at the stop. To consider the capacitated UE considering the relation between the FIFO boarding priority and vehicle capacity constraint, we apply a "soft-capacity" cost (Nguyen et al., 2001). This soft capacity cost function allows some violation of the predefined vehicle capacity, but the violation will be penalized and affect the cost of the path in the next iteration. The penalty of the soft capacity cost function allows not to assigning passengers on the alternatives having the lower priority of boarding, which finally leads to the solution of the capacitated transit deterministic user equilibrium (DUE) or SUE problems. For the main transit assignment models, we proposed path- and hyperpath-based methods and a self-adaptive method considering deterministic and stochastic passenger behaviors. First, we developed the hyperpath-based assignment method by Noh et al. (2012b). For the FIFO transit passenger behavior, typically accompanying asymmetric (non-separable) cost relation, we also introduce a diagonalization technique (Sheffi, 1985) with the method of successive average (MSA) assignment technique. As expecting a better performance, second, we introduced the path-based assignment models using gradient projection. For the FIFO passenger behavior on boarding, we considered the same diagonalization approach used in the hyperpath-based assignment model and a full-Hessian scaling matrix in the gradient projection. By utilizing a full path set for each O-D pair, a better performance is guaranteed with the path-based model but the diagonalization technique may result in longer iterations. For improving the diagonalization steps, third, we explored several other possible methods. Above all, we proposed the better initial solution (BIS) model which assigns the initial flows on the priority path over congested links and also maintains feasible flows below the capacity constraint. On the other hand, we also added two additional assignment models to improve the diagonalization technique. One utilizes a full Hessian scaling matrix in the proposed path-based assignment model instead of diagonalization and the other is the self-adaptive gradient projection (SAGP) model introduced by Chen et al. (2012) which does not require a scaling matrix by optimizing the step-size in the path-based projection model. For improving the SAGP model, we modified the SAGP model. First, we applied the SAGP at a disaggregate level for each O-D pair as expecting a compact set of path alternatives limited by each O-D pair, called disaggregate self-adaptive gradient projection (DSAGP). Second, we applied a type of diagonalization technique in the SAGP model by maintaining the residual capacities for the estimated flows in the next iteration. Beyond just a single model development, the proposed transit assignment models not only showed various possibilities of the transit assignment, but also showed which model is more efficient and practical in terms of a real application. A computational model structure using the proposed models was mainly designed for an effective model development by sharing numerous components as well as maintaining the efficient data structure. The nine combination models based on the proposed three main models (hyperpath- and path-based and DSAGP assignment models) and the efficient BIS technique for solving the problems were tested and analyzed on a sample network and a partial Sacramento regional transit network

Trip-based path algorithms using the transit network hierarchy

In this paper, we propose a new network representation for modeling schedule-based transit systems. The proposed network representation, called trip-based, uses transit vehicle trips as network edges and takes into account the transfer stop hierarchy in transit networks. Based on the trip-based network, we propose a set of path algorithms for schedule-based transit networks, including algorithms for the shortest path, a logit-based hyperpath, and a transit A*. The algorithms are applied to a large-scale transit network and shown to have better computational performance compared to the existing labeling algorithms

Enzyme activity engineering based on sequence co-evolution analysis

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection. A repertoire of mutations was selected for fine-tuning enzyme activities to adapt to varying environments during the evolution. Here, we devised a strategy called sequence co-evolutionary analysis to control the efficiency of enzyme reactions (SCANEER), which scans the evolution of protein sequences and direct mutation strategy to improve enzyme activity. We hypothesized that amino acid pairs for various enzyme activity were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. SCANEER successfully predicted the enzyme activities of beta-lactamase and aminoglycoside 3 '-phosphotransferase. SCANEER was further experimentally validated to control the activities of three different enzymes of great interest in chemical production: cis-aconitate decarboxylase, alpha-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase. Activity-enhancing mutations that improve substratebinding affinity or turnover rate were found at sites distal from known active sites or ligand-binding pockets. We provide SCANEER to control desired enzyme activity through a user-friendly webserver.11Nsciescopu

The application of an integrated behavioral activity-travel simulation model for pricing policy analysis

This chapter demonstrates the feasibility of applying an integrated microsimulation model of activitytravel demand and dynamic traffic assignment for analyzing the impact of pricing policies on traveler activity-travel choices. The model system is based on a dynamic integration framework wherein the activity-travel simulator and the dynamic traffic assignment model communicate with one another along the continuous time axis so that trips are routed and simulated on the network as and when they are generated. This framework is applied to the analysis of a system-wide pricing policy for a small case study site to demonstrate how the model responds to various levels of pricing. Case study results show that trip lengths, travel time expenditures, and vehicle miles of travel are affected to a greater degree than activity-trip rates and activity durations as a result of pricing policies. Measures of change output by the model are found to be consistent with elasticity estimates reported in the literature