System dynamics of urban traffic based on its parking-related-states

The urban parking and the urban traffic systems are essential components of the overall urban transportation structure. The short-term interactions between these two systems can be highly significant and influential to their individual performance. The urban parking system, for example, can affect the searching-for-parking traffic, influencing not only overall travel speeds in the network (traffic performance), but also total driven distance (environmental conditions). In turn, the traffic performance can also affect the time drivers spend searching for parking, and ultimately, parking usage. In this study, we propose a methodology to model macroscopically such interactions and evaluate their effects on urban congestion.The model is built on a matrix describing how, over time, vehicles in an urban area transition from one parking-related state to another. With this model it is possible to estimate, based on the traffic and parking demand as well as the parking supply, the amount of vehicles searching for parking, the amount of vehicles driving on the network but not searching for parking, and the amount of vehicles parked at any given time. More importantly, it is also possible to estimate the total (or average) time spent and distance driven within each of these states. Based on that, the model can be used to design and evaluate different parking policies, to improve (or optimize) the performance of both systems.A simple numerical example is provided to show possible applications of this type. Parking policies such as increasing parking supply or shortening the maximum parking duration allowed (i.e., time controls) are tested, and their effects on traffic are estimated. The preliminary results show that time control policies can alleviate the parking-caused traffic issues without the need for providing additional parking facilities. Results also show that parking policies that intend to reduce traffic delay may, at the same time, increase the driven distance and cause negative externalities. Hence, caution must be exercised and multiple traffic metrics should be evaluated before selecting these policies.Overall, this paper shows how the system dynamics of urban traffic, based on its parking-related-states, can be used to efficiently evaluate the urban traffic and parking systems macroscopically. The proposed model can be used to estimate both, how parking availability can affect traffic performance (e.g., average time searching for parking, number of cars searching for parking); and how different traffic conditions (e.g., travel speed, density in the system) can affect drivers ability to find parking. Moreover, the proposed model can be used to study multiple strategies or scenarios for traffic operations and control, transportation planning, land use planning, or parking management and operations.     

A two-stage approach to modeling vacant taxi movements

In this paper, a two-stage modeling approach is proposed to predict vacant taxi movements in searching for customers. The taxi movement problem is formulated into a two-stage model that consists of two sub-models, namely the first and second stage sub-models. The first stage sub-model estimates the zone choice of vacant taxi drivers for customer-search and the second stage sub-model determines the circulation time and distance of vacant taxi drivers in each zone by capturing their local customer-search decisions in a cell-based network within the zone chosen in the first stage sub-model. These two sub-models are designed to influence each other, and hence an iterative solution procedure is introduced to solve for a convergent solution. The modeling concept, advantages, and applications are illustrated by the global positioning system data of 460 Hong Kong urban taxis. The results demonstrate that the proposed model formulation offers a great improvement in terms of root mean square error as compared with the existing taxi customer-search models, and show the model capabilities of predicting the changes in vacant taxi trip distributions with respect to the variations in the fleet size and fare. Potential taxi policies are investigated and discussed according to the findings to provide insights in managing the Hong Kong taxi market.     

Evaluation of urban accessibility indicators based on internal and external environmental costs

This paper estimates urban accessibility considering types of transport and destinations, taking into account the internal travel time costs, and the ensuing external environmental impacts. Based on online and local surveys, an accessibility function is developed to allow for the construction of an accessibility curve for each transport mode that decreases with distance to represent decaying accessibility. An external environmental impact is associated with the accessibility indicators, taking into account the influence of the cold-start emissions that are particularly relevant for short-distance trips. The methodology is applied to neighborhoods in Lisbon, Portugal, with significant differences in their urban planning, mobility patterns, concentration of services and availability of public transportation.     

Modeling isoexposure to transit users for market potential analysis

Transit operators face a difficult fiscal environment and an imperative to contribute to urban sustainability. Under these circumstances, operators must find innovative ways to make public transportation attractive to broader segments of the public, while simultaneously trying to raise revenue to reduce reliance on public subsidies. Development of commercial partnerships is seen as a promising way to achieve these goals. Previous research has examined the potential of using geodemographics to assist transit agencies in the task of identifying potential partners for developing mutually beneficial commercial agreements. In this paper we describe an approach to model isoexposure to transit users as a tool to assess market potential. The approach is based on the analysis of walking behavior of transit users, and specifically distance walked at the end of their transit trip. Spatial modeling is used to geographically project estimates of walking distance for a desired demographic profile at a specific transit facility. After expanding the estimates using sample weights, overlays of these estimates can be used to generate variations in exposure to transit travelers at different locations in space. The approach is demonstrated using the case of Metro users in Montreal, Canada. The case study demonstrates the use of isoexposure profiles as a novel approach to generate marketing intelligence. This should be of interest to transit agencies and businesses interested in developing partnerships.     

The valuation of commuter travel time savings for car drivers: evaluating alternative model specifications

The empirical valuation of travel time savings is a derivative of the ratio of parameter estimates in a discrete choice model. The most common formulation (multinomial logit) imposes strong restrictions on the profile of the unobserved influences on choice as represented by the random component of a preference function. As we progress our ability to relax these restrictions we open up opportunities to benchmark the values derived from simple (albeit relatively restrictive) models. In this paper we contrast the values of travel time savings derived from multinomial logit and alternative specifications of mixed (or random parameter) logit models. The empirical setting is urban car commuting in six locations in New Zealand. The evidence suggests that less restrictive choice model specifications tend to produce higher estimates of values of time savings compared to the multinomial logit model; however the degree of under-estimation of multinomial logit remains quite variable, depending on the context.     

A travel time reliability model of urban expressways with varying levels of service

Urban expressways usually experience several levels of service (LOS) because of the stop-and-go traffic flow caused by congestion. Moreover, multiple shock waves generate at different LOS interfaces. The dynamic of shock waves strongly influences the travel time reliability (TTR) of urban expressways. This study proposes a path TTR model that considers the dynamic of shock waves by using probability-based method to characterize the TTR of urban expressways with shock waves. Two model parameters are estimated, namely distribution of travel time (TT) per unit distance and travel distances in different LOS segments. Generalized extreme value distribution and generalized Pareto distribution are derived as distributions of TT per unit distance for six different LOS. Distribution parameters are estimated by using historical floating car data. Travel distances in different LOS segments are calculated based on shock wave theory. The range of TT along the path, which can help drivers arrange their trips, can be obtained from the TTR model. Finally, comparison is made among the proposed TTR model, generalized Pareto contrast model, which does not consider different LOS or existence of shock waves, and normal contrast model, which assumes TT per unit distance as normal distribution without considering shock wave. Results show that the proposed model achieves higher prediction accuracy and reduces the prediction range of TT. The conclusions can be further extended to TT prediction and assessment of measures to improve reliability of TT in a network.     

Estimating time dependent O‐D trip tables during peak periods

Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real‐time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin‐destination (O‐D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O‐D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O‐D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding‐off, and, 2) low ratio of total number of trips to total number of O‐D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O‐D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed.     

The impact of urban form on commuting in large Chinese cities

Studies on cities in Europe and the United States have demonstrated that travel behaviour is influenced by urban form. Based on these findings policies steering the shape of cities have been proposed to reduce urban transport emissions and limit congestion. Such policies can also be relevant for the rapidly growing and motorising Chinese cities. Yet, empirical evidence on the relationships between urban form and car usage is scarce for the specific Chinese context that is characterised by high densities, fast development and strong government steering. Using novel crowd-sourced datasets we study the impact of several urban form variables (city size, urban density, land-use mix, polycentricity and spatial clustering) on the cost of commuting expressed in time and distance. The results show that city size and spatial clustering are important determinants of commuting: large cities without clear clusters of businesses and other facilities have longer average commuting times and distances. Increased prosperity also adds to longer and lengthier commutes. Spatial planning measures that maintain or reinforce high-density clusters can help limiting commuting distance and time. Current sprawled urban development may have long-term, negative consequences for the accessibility and liveability of Chinese cities and could hamper their economic potential.     

A time-dependent freight tour synthesis model

This paper introduces a model of urban freight demand that seeks to estimate tour flows from secondary data sources e.g., traffic counts, to bypass the need for expensive surveys. The model discussed in this paper, referred as Freight Tour Synthesis (FTS), enhances current techniques by incorporating the time-dependent tour-based behavior of freight vehicles, and the decision maker’s (e.g., metropolitan planning agency planner) preferences for different sources of information. The model, based on entropy maximization theory, estimates the most likely set of tour flows, given a set of freight trip generation estimates, a set of traffic counts per time interval, and total freight transportation cost in the network. The type of inputs used allows the assessment of changes in infrastructure, policy and land use. The ability of the model to replicate actual values is assessed using the Denver Region (CO) as a case study.     

Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal

This paper addresses the relations between travel behavior and land use patterns using a Structural Equations Modeling (SEM) framework. The proposed model structure draws on two earlier models developed for Lisbon and Seattle which show significant effects of land use patterns on travel behavior. The travel behavior variables included here are multifaceted including commuting distance, car ownership, the amount of mobility by mode (car, transit and non-motorized modes), both in terms of total kilometers travelled and number of trips. The model also includes a travel scheduling variable, which is the total time spent between the first and last trips to reflect daily constraints in time allocation and travel.The modeled land use variables measure the levels of urban concentration and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants/residents, the transport supply levels, transit and road infrastructure, and accessibility indicators. The land use patterns are described both at the residence and employment zones of each individual included in the model by using a factor analysis technique as a data reduction and multicollinearity elimination technique. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The results obtained show that people with different socioeconomic characteristics tend to work and live in places of substantially different urban environments. But besides these socioeconomic self-selection effects, land use variables significantly affect travel behavior. More precisely the effects of land use are in great part passed thru variables describing long term decisions like commuting distance, and car ownership. These results point to similar conclusions from the models developed for Lisbon and Seattle and thus give weight to the use of land use policies as tools for changing travel behavior.     

The travel and environmental implications of shared autonomous vehicles,using agent-based model scenarios

Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles.This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.     

A fuzzy-logic-based system for freeway bottleneck severity diagnosis in a sensor network

It is essential for local traffic jurisdictions to systematically spot freeway bottlenecks and proactively deploy appropriate congestion mitigation strategies. However, diagnostic results may be influenced by unreliable measurements, analysts’ subjective knowledge and day-to-day traffic pattern variations. In order to suitably address these uncertainties and imprecise data, this study proposes a fuzzy-logic-based approach for bottleneck severity diagnosis in urban sensor networks. A dynamic bottleneck identification model is first proposed to identify bottleneck locations, and a fuzzy inference approach is then proposed to systematically diagnose the severities of the identified recurring and non-recurring bottlenecks by incorporating expert knowledge of local traffic conditions. Sample data over a 1-month period on an urban freeway in Northern Virginia was used as a case study for the analysis. The results reveal that the proposed approach can reasonably determine bottleneck severities and critical links, accounting for both spatial and temporal factors in a sensor network.     

Moving between mobility cultures: what affects the travel behavior of new residents?

This paper analyzes the complex interdependencies between residential relocation and daily travel behavior by focusing on modal change. To help explain changes in daily travel patterns after a long distance move between cities the concept of urban mobility cultures is introduced. This comprehensive approach integrates objective and subjective elements of urban mobility, such as urban form and socio-economics on the one hand, and lifestyle orientations and mode preferences on the other, within one socio-technical framework. Empirically, the study is based on a survey conducted among people who recently moved between the German cities Bremen, Hamburg and the Ruhr area. Bivariate analyses and linear multiple regression models are applied to analyze changes in car, rail-based and bicycle travel. This is done by integrating variables that account for urban mobility cultures and controlling for urban form, residential preferences and socio-demographics. A central finding of this study is, that changes in the use of the car and rail-based travel are much more dependent on local scale, such as neighborhood type and residential preferences, whereas cycling is more affected by city-wide attributes, which we addressed as mobility culture elements.     

Parking policy,parking location decisions and the distribution of congestion

The paper develops and tests a model which characterizes the parking location decisions of individual tripmakers. The model is designed to offer information concerning the effects of alternative parking policies on parking location decisions and therefore the effects on the distribution of congestion in an urban area.Own price, time price and full price elasticities for alternative parking locations are estimated. The own price elasticity is found to rise with distance from the destination point while the time price elasticity falls with distance. The full price elasticity is found to be relatively stable.One is able to determine from the calculated elasticities, the effects of alternative parking policies such as raising parking fees, time restrictions, or increasing search or transaction costs on the distribution of individuals consuming parking services; from this one can infer the impact on the distribution of congestion.The paper also offers some explanation for the low elasticity of auto use with respect to changes in parking costs found in some modal choice studies.The author is indebted to Adolf Buse, Ken Norrie and Richard Westin for helpful comments and criticism.     

International experience with speed limits during and prior to the energy crisis of 1973–4

This paper presents an artificial neural network (ANN) based method for estimating route travel times between individual locations in an urban traffic network. Fast and accurate estimation of route travel times is required by the vehicle routing and scheduling process involved in many fleet vehicle operation systems such as dial‐a‐ride paratransit, school bus, and private delivery services. The methodology developed in this paper assumes that route travel times are time‐dependent and stochastic and their means and standard deviations need to be estimated. Three feed‐forward neural networks are developed to model the travel time behaviour during different time periods of the day‐the AM peak, the PM peak, and the off‐peak. These models are subsequently trained and tested using data simulated on the road network for the City of Edmonton, Alberta. A comparison of the ANN model with a traditional distance‐based model and a shortest path algorithm is then presented. The practical implication of the ANN method is subsequently demonstrated within a dial‐a‐ride paratransit vehicle routing and scheduling problem. The computational results show that the ANN‐based route travel time estimation model is appropriate, with respect to accuracy and speed, for use in real applications.     

Estimating Freight Transport Price Elasticity in Multi-mode Studies: A Review and Additional Results from a Multimodal Network Model

Abstract

The paper presents a detailed comparative review of price/cost elasticity estimates published in a number of studies on multi-mode freight transport demands. It attempts to determine which factors could explain the wide diversity of estimates: data aggregation, diversity of markets, and methodology. It also presents new estimates for rail, road, and waterway modes, derived from a multimodal freight network model of the Rhine area market. Direct and cross-elasticities are estimated for 11 groups of commodities and per distance category. The results are critically assessed by comparison with the reviewed studies. The paper concludes with a few recommendations about meaningful uses of existing estimates and the need for additional experiments with different methodologies applied on a common data basis.     

Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach

Concerns over transportation energy consumption and green-household gas (GHG) emissions have prompted a growing body of research into the influence of built environment on travel behavior. Studies on the relationship between land use and travel behavior are often at a certain aggregated spatial unit such as traffic analysis zone (TAZ), spatial issues occur among individuals clustered within a zone because of the locational effects. However, recognition of the spatial issues in travel modeling was not sufficiently investigated yet. The object of this study is twofold. First, a multilevel hazard model was applied to accommodate the spatial context in which individuals generate commuting distance. Second, this research provides additional insights into examine the effects of socio-demographics and built environment on commuting distance. Using Washington metropolitan area as the case, the built environment measures were calculated for each TAZ. To estimate the model parameters, the robust maximum likelihood estimation method for a partial function was used, and the model results confirmed the important roles that played by the TAZ and individual level factors in influencing commuting distance. Meanwhile, a comparison among the general multilevel model, single level and multilevel hazard models was conducted. The results suggest that application of the multilevel hazard-based model obtains significant improvements over traditional model. The significant spatial heterogeneity parameter indicates that it is necessary to accommodate the spatial issues in the context of commuting distance. The results are expected to give urban planners a better understanding on how the TAZ and individual level factors influence the commuting distance, and consequently develop targeted countermeasures.     

A macroscopic theory for unsignalized intersections

The following paper presents a dynamic macroscopic model for unsignalized intersections which accounts for time-limited disruptions in the minor stream flow, even in free-flow conditions when the average flow demand is satisfied. It introduces a deterministic fictive traffic light to represent an average alternating sequence of available and busy time periods for insertion depending on the major stream flow. Two allocation schemes of the total outflow during green periods are developed to model the influence or non-influence of the minor stream over the major stream flow. The aggregation of the resulting dynamic flow variations gives relevant capacity values. Moreover, the model predicts accurate average vehicle delay and queue length estimates compared to theoretical and empirical data. It has three easy-to-measure parameters and can be integrated into a dynamic macroscopic simulation tool for urban networks.     

A cell-based logit-opportunity taxi customer-search model

This paper proposes a cell-based model to predict local customer-search movements of vacant taxi drivers, which incorporates the modeling principles of the logit-based search model and the intervening opportunity model. The local customer-search movements were extracted from the global positioning system data of 460 Hong Kong urban taxis and inputted into a cell-based taxi operating network to calibrate the model and validate the modeling concepts. The model results reveal that the taxi drivers’ local search decisions are significantly affected by the (cumulative) probability of successfully picking up a customer along the search route, and that the drivers do not search their customers under the random walk principle. The proposed model helps predict the effects of the implementation of the policies in adjusting the taxi fleet size and the changes in passenger demand on the customer-search distance and time of taxi drivers.     

Measuring crowding-related comfort in public transport

ABSTRACT

In this paper we focus on the estimation of crowding in public transport – specifically urban rail systems – and its effect on perceived comfort. It is different from similar studies in the method it employs for estimating crowding levels in vehicles. Specifically, we formulate a function of time and location, which uses only passenger embarking data to estimate the number of passengers in vehicles. Then we convert the estimated crowding values into perceived discomfort levels by trip section. Our method depends on hourly seasonality assumptions but provides good estimates of crowding in urban rail systems even when passenger alighting data is not available. We illustrate the implementation of our model with the example of the Istanbul Metro system.