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<p>Travel demand management policies such as congestion charges encourage car-users to change among other things route, mode and departure time. Departure time may be especially affected by time-varying charges, since car-users can avoid high peak hour charges by travelling earlier or later, so called peak spreading effects. Conventional transport models do not include departure time choice as a response. For evaluation of time-varying congestion charges departure time choice is essential.</p><p>In this thesis a transport model called SILVESTER is implemented for Stockholm. It includes departure time, mode and route choice. Morning trips, commuting as well as other trips, are modelled and time is discretized into fifteen-minute time periods. This way peak spreading effects can be analysed. The implementation is made around an existing route choice model called CONTRAM, for which a Stockholm network already exists. The CONTRAM network has been in use for a long time in Stockholm and an origin-destination matrix calibrated against local traffic counts and travel times guarantee local credibility. On the demand side, an earlier developed departure time and mode choice model of mixed logit type is used. It was estimated on CONTRAM travel times to be consistent with the route choice model. The behavioural response under time-varying congestion charges was estimated from a hypothetical study conducted in Stockholm.</p><p>Paper I describes the implementation of SILVESTER. The paper shows model structure, how model run time was reduced and tests of convergence. As regards run time, a 75% cut down was achieved by reducing the number of origin-destination pairs while not changing travel time and distance distributions too much.</p><p>In Paper II car-users underlying preferred departure times are derived using a method called reverse engineering. This method derives preferred departure times that reproduce as well as possible the observed travel pattern of the base year. Reverse engineering has previously only been used on small example road networks. Paper II shows that application of reverse engineering to a real-life road network is possible and gives reasonable results.</p> / Silvester

Congestion charging has drawn considerable attention of transport analysts and policymakers as a mean of relieving urban traffic congestion. Proper prediction of the impacts of charging is necessary for policy makers to take right decisions. A European project named SILVERPOLIS have been introduced in this connection to describe state-of-practice in modelling effects of congestion charging and to identify features of transport models that are crucial for reliable forecasting of effects of congestion charging. This master thesis is a part of the SILVERPOLIS project, where Stockholm congestion charging scheme has been analysed using two different types of dynamic simulators: METROPOLIS and SILVESTER. The simulations are based on traffic data collected before and after the Stockholm congestion charging trial performed in spring 2006. The result of simulation suggests that METROPOLIS, which has been used for predicting effects of congestion charging in Ile-de-France, manages well to forecast the consequences of congestion charging for Stockholm. Comparison with SILVESTER model disclosed that, although calibration results of the two models differs in some respect, both models give similar results regarding impacts of congestion charging. The different modelling features and assumptions have been described for the two models. Despite the fact that the two models vary a lot in their assumptions and modelling style, both of them has proved to be good at describing the effect of congestion charging.

When faced with an uncertain network, travelers adjust departure time as well as route choices in response to real-time traveler information. Previous studies on algorithm design focus on adaptive route choices and cannot model adaptive departure time choices (DTC). In this thesis, the optimal adaptive departure time and route choice problem in a stochastic time-dependent network is studied. Travelers are assumed to minimize expected generalized cost which is the sum of expected travel cost and arrival delay costs. The uncertain network is modeled by jointly distributed random travel time variables for all links at all time periods. Real-time traveler information reveals realized link travel times and thus reduces uncertainties in the network. The adaptive departure time and route choice process is conceptualized as a routing policy, defined as a decision rule that specifies what node to take next at each decision node based on realized link travel times and the current time. Waiting at origin nodes is allowed to model DTCs that are dependent on traveler information. Departure time is a random variable rather than fixed as in previous studies. A new concept of action time is introduced, which is the time-of-day when a traveler starts the DTC decision process. Because of the efforts involved in processing information and making decisions, a cost could be associated with a departure made after the action time. An algorithm is designed to compute the minimum expected generalized cost routing policy and the corresponding optimal action time, from all origins to a destination for a given desired arrival time window. Computational tests are carried out on a hypothetical network and randomly generated networks. It is shown that adaptive DTCs lead to less expected generalized cost than fixed DTCs do. The benefit of adaptive DTC is larger when the variance of the travel time increases. The departure time distribution is more concentrated with a larger unit cost of departure delay. A wider arrival time window leads to a more dispersed departure time distribution, when there is no departure penalty.

Travel demand management policies such as congestion charges encourage car-users to change among other things route, mode and departure time. Departure time may be especially affected by time-varying charges, since car-users can avoid high peak hour charges by travelling earlier or later, so called peak spreading effects. Conventional transport models do not include departure time choice as a response. For evaluation of time-varying congestion charges departure time choice is essential. In this thesis a transport model called SILVESTER is implemented for Stockholm. It includes departure time, mode and route choice. Morning trips, commuting as well as other trips, are modelled and time is discretized into fifteen-minute time periods. This way peak spreading effects can be analysed. The implementation is made around an existing route choice model called CONTRAM, for which a Stockholm network already exists. The CONTRAM network has been in use for a long time in Stockholm and an origin-destination matrix calibrated against local traffic counts and travel times guarantee local credibility. On the demand side, an earlier developed departure time and mode choice model of mixed logit type is used. It was estimated on CONTRAM travel times to be consistent with the route choice model. The behavioural response under time-varying congestion charges was estimated from a hypothetical study conducted in Stockholm. Paper I describes the implementation of SILVESTER. The paper shows model structure, how model run time was reduced and tests of convergence. As regards run time, a 75% cut down was achieved by reducing the number of origin-destination pairs while not changing travel time and distance distributions too much. In Paper II car-users underlying preferred departure times are derived using a method called reverse engineering. This method derives preferred departure times that reproduce as well as possible the observed travel pattern of the base year. Reverse engineering has previously only been used on small example road networks. Paper II shows that application of reverse engineering to a real-life road network is possible and gives reasonable results. / <p>QC 20170222</p> / Silvester

Value pricing is now an accepted strategy for congestion and demand management in metropolitan areas. Along with alternate congestion management strategies, many transportation agencies have started looking at value pricing as a method to help financial shortfalls of new congestion management projects. Value pricing allows revenue collected from toll facilities to reduce operational concerns with underutilized High Occupancy Vehicle (HOV) facilities and relieves environmental concerns by reducing travel demand. Recently, transportation agencies have become increasingly interested in a high-occupancy toll (HOT) lane value pricing system with time-dependent tolls or dynamic tolls that change by the congestion level. However, there is a lack of proper travel demand forecasting tools that can evaluate and determine the impacts of pricing on travelers' decision in relation to congestion. The current methods use aggregated and zonal based approaches that lack the capability of tracing individual travelers through the supply network in order to capture his/her travel decisions as it pertains to the estimated cost for toll usage. The conventional models do not consider individual traveler socio-economic characteristics, particularly the heterogeneous value of time (VOT). TRANSIMS (Transportation Analysis Simulation System) differs from current travel demand forecasting methods in its underlying concepts and structure. These differences include a consistent and continuous representation of time, a detailed representation of persons and households, time-dependent routing, and a person-based Microsimulator. The TRANSIMS Microsimulator is the only simulation tool that maintains the identity of the traveler throughout the simulation and is capable of accessing the database of each individual (e.g., income, age, trip purpose). It traces the movement of people as well as vehicles on a second-by-second basis. Although TRANSIMS environment has significantly improved over the past few years, there are still issues that need to be improved upon including: the pricing of a HOT lane with dynamic tolls and the rescheduling of activities (i.e., departure time choice model) in response to network conditions. The primary objectives of this study are to improve functions of TRANSIMS by modifying source codes in order to utilize non-linear, individual VOT function in route choice of a HOT lane value pricing system, to implement 15-min dynamic tolls that vary by level of service (i.e., volume/capacity ratio) in the HOT lane(s) and to develop departure time choice model. Testing the proposed methodologies using real-world data as case studies and evaluating the impacts of dynamic tolls and/or departure time choice model are other objectives of this study. The test site of the HOT lane system is a segment of I-5 northbound from Hwy 217 to I-405 near the central business district (CBD) in Portland metropolitan region, Oregon. The experimental analyses of the application of dynamic tolls and individual VOT demonstrate the feasibility of the proposed simulation methodology. The outputs from the microscopic analysis clearly indicate the effectiveness of the analysis in scrutinizing travelers' route choice behavior based on different socio-economic and travel characteristics when different toll rates are applied. The effects of individual VOT on route choice are consistent with intuition; that is, travelers with higher VOTs are more likely to choose the HOT lane(s). In addition, the impacts of various tolls on route choice are analyzed on the basis of socio-economic and trip characteristics of each traveler. In addition to the development of the dynamic value pricing along with individual VOT, the departure time choice model is also developed. The proposed method is a post-processing of route choice and represents a sequential decision making process of travelers who want to depart early or late based on congestion, individual attributes and activity characteristics. This paper presents the results of a departure time choice model and its impacts on a HOT lane system using Portland, Oregon as a case study. The results show that 13.9% of households did change their departure time because of congestion and/or tolls. / Ph. D.