Private tollroads in urban areas

Governments of all persuasions are increasingly seeking the participation of the private sector in the supply of transport facilities and services. Private sector participation in the financing, construction, operation and maintenance of infrastructure is considered a serious option in a number of countries in the search for ways of providing much needed investment which would otherwise be deferred. This paper considers some economic and financial problems in the private sector provision of major road infrastructure within urban areas. The main issues are attaching prices (i.e. tolls) to the provision of the service, the value of government rights which are being given up either permanently or temporarily, and the identification required by the promoters of the cost of capital which is essential information in establishing the risk. Broader environmental and equity issues are not addressed. If the approach to establishing a private presence in a previously public supply context is handled properly from the outset, the benefits can be significant. Contrarily however, the prospects could be quite undesirable if badly managed, despite the presence of an extended public purse.requests for offprints     

A regression model for bus running times in suburban areas of winnipeg

To plan new bus routes in suburban areas, expected bus running times on these routes are needed. Using most readily available relevant variables, a regression model is developed for estimating bus running times. The model is conceptually reasonable and it was tested using data other than that used for estimation.     

Performance evaluation and obstacle factors analysis of urban public transport priority

ABSTRACT

Through the comprehensive consideration of four subsystems – overall development level, infrastructure construction, public transportation service level and policy support – an index system of public transport priority performance evaluation is established. A performance evaluation of bus priority implementation in Wuhan City from 2007 to 2016 is carried out by applying the difference coefficient CRITIC-TOPSIS model. The obstacle factor model is also used to diagnose the factors affecting the priority performance of urban public transport. The research results show that, during this decade, the comprehensive performance of Wuhan City’s public transport priority developed from poor to medium, then to good and finally to excellent. The overall development level and infrastructure construction performance subsystems have the highest obstacle degree, followed by public transportation service levels and policy support performance subsystems. The research idea and method of this paper provide a realistic basis for promoting the priority performance of urban public transport.     

Multi‐criteria analysis procedure for sustainable mobility evaluation in urban areas

This paper proposes a procedure to evaluate sustainable mobility in urban areas. A set of indicators according to three dimensions of sustainability, i.e., environment, economics, and social aspects, are proposed to evaluate mobility in urban areas. The sustainable mobility evaluation is based on an Index calculated through a weighted multi‐criteria combination procedure. A group of specialists in Brazil was involved in the development of the Index by defining the weights for the criteria. An application of the methodology in the city of Belo Horizonte, capital of the State of Minas Gerais, with 2.24 million inhabitants, is presented to validate the methodology.     

The distribution of truck trips and commodity flow in urban areas: A gravity model analysis

In this paper, the distributions of urban truck trips and urban commodity flow are analyzed using a gravity model formulation. Models are calibrated using data for Melbourne, Australia, and the results are assessed, firstly with respect to the applicability of the gravity model to these applications, and secondly with respect to the differences which are revealed between various truck trip purposes and commodities. The results suggest that the gravity model is suited to analyzing the distribution of truck trips within urban areas, and also the distribution of those commodities whose origins or destinations are not restricted to a small number of locations.     

Truck trip generation in small- and medium-sized urban areas

This paper describes procedures to develop truck trip generation (TTG) rates for small- and medium-sized urban areas and its implications. Ordinary least squares models are used to develop separate truck production and attraction equations with the number of employees as the independent variable for three industrial groups – retail, transportation and warehousing, and manufacturing. Results from this research indicate that number of employees is a statistically significant predictor, and has significant explanatory power in predicting the number of truck trips produced and attracted. The rates developed in this study are also found to be significantly different from rates developed in other studies with the implication that caution needs to be taken when transferring TTG rates. The rates are applied in a travel demand model as the initial step of incorporating truck traffic into the modeling process.     

Characterization and evolution of spatial density patterns in urban areas

This paper examines the spatial patterns of population density, household automobile ownership and other socio-demographic variables that affect urban travel, as a function of distance from the central city core. Spatial density functions provide a useful characterization of urban structure, and of its evolution when taken at different time intervals. Analysis of the data from four case cities (Austin, Atlanta, Dallas, Phoenix) for 1960, 1970 and 1980 reveals continuing overall dispersion away from the traditional central core, accompanied by the densification of formerly low-density suburbs. This presents implications for high congestion levels in the densifying suburban communities, comparable to those typically associated with the CBD. In addition, the analysis has captured the continuing growth of average household automobile ownership and revealed a distinct spatial pattern that seems to be robust across the case areas considered, as well as within radial corridors in the one case that was so analyzed (Austin).List of symbols Y gross population density in census tractt - X _t distance in miles from the center of tract to the CBD - parameter representing the central business district density - y density gradient parameter - X₀, X₁, X2, and X3 locations of the knots in a three segment division of the x-axis - D_i dummy variable defined for thei-th segment - ₁ normally distributed disturbance term,a _i,b _i,c _i,d _i,i = 1, 2, 3 — parameters to be estimated - A _t area of census tractt     

Bus journey times in medium size urban areas

The predictive accuracy of the models based on the fundamental relation between journey time and passenger demand can be improved through data disaggregation or route segmentation. Primary reason for this is the improvement in the estimates of stopping delays and delays due to passenger boarding and/or alighting (dwell time). Both Poisson and Negative Binomial model estimates of stoppings for passenger boarding and alighting are shown to improve with disaggregation. These improvements, however, contribute little to the overall predictability of the fundamental models which are useful for gaining insight into the significance and variability of the stopping delays and dwell time, or testing sensitivity to changes in the long term. Site or route specific models of journey times which have better predictive capability exist, and may be used for short-run planning. However, the interchangeability and performance over time of the latter, have to be evaluated before making definitive conclusions.     

Modeling access to public transport in urban areas

It is important to measure public transport accessibility to help improve the sustainability of transport systems in metropolitan areas. Although many studies have defined different approaches for measuring public transport accessibility, there have been limited methods developed for measuring accessibility levels that incorporate spatial aspects. Population density is an important distributional indicator that has also been ignored in previous methods developed for quantifying accessibility. This paper outlines the research context for measurement of public transport accessibility and then describes a methodology developed as well as an application the Public Transport Accessibility Index in Melbourne area, Australia. Using the Victorian Integrated Survey of Travel and Activity dataset, we applied separate‐ordered logit regression models to examine how the new index performs with a series of predictor variables compared with two existing approaches. Key findings indicate that there is a higher probability of public transport patronage in areas with higher levels of accessibility. Furthermore, it was found using statistical modelling that the new index produces better results compared with previous approaches. Copyright © 2016 John Wiley & Sons, Ltd.     

Development of interregional cooperation in the field of urban and suburban transport under the auspices of the UN

The article considers specific features, the importance and the problems of urban and suburban transport with reference to international economic, scientific and technological cooperation. It also describes methods and means for utilizing the opportunities for cooperation within the framework of the UN Regional Economic Commissions with a more active involvement of CODATU and UNCHS (Habitat) and the Habitat Executive Bureau in Moscow. The article identifies and classifies the main tasks of the development of urban public transport in cities with varying degree of motorization as well as identifies the priorities of international cooperation within the UN.     

Safety-based path finding in urban areas for older drivers and bicyclists

This paper presents a safety-based path finding methodology for older drivers and bicyclists in an urban area. The paths are estimated based on costs consisting of both safety and travel time. Safety is evaluated against potential risk of a crash involving an older driver (or a bicyclist) with other vehicles present on the road. To accomplish this, simple formulations are developed for safety indicators of streets and intersections, which are actually generic irrespective of the type of road user. Traffic attributes such as speed and density, driver attributes such as perception-reaction time and street attributes of length and tire-to-road friction coefficient are taken into account in building the safety indicators. Thus, the safety indicators do not necessarily require historical crash data which may or may not be available during path finding. Subsequently, a multi-objective shortest path algorithm is presented that identifies the best path (the non-inferior path) from amongst a set of selected safest paths with due considerations to travel time incurred on each. A simple application example of the proposed methodology is demonstrated on an existing street network system from the City of College Station, Texas. The contributions of this research are twofold – first, the safety indicators can be used by planners in determining high crash potential sites – streets and/or intersections – and second, the safety-based path finding methodology developed in this paper can be integrated with modern day route planning devices and tools in guiding older drivers and bicyclists within an Intelligent Transportation Systems framework.     

A heuristic model of bounded route choice in urban areas

There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, ‘good enough’ decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks.     

Exploring the issue of integrating logistics and traffic control in urban areas

This paper proposes an optimization framework for urban transportation networks’ (re-)design which explicitly takes into account the specific decision-making processes of ordinary users and logistic operators. Ordinary users are typically commuters whose travels consist of well-defined pairs of origin and destination points, while logistic operators make deliveries at multiple locations. Obviously, these two user classes have different objectives and scopes of action. These differences are seldom considered in traffic research since most models aggregate the flow demand in OD matrices and use assignment models to predict the response of all users as if the dynamics of their optimization processes were of the same nature. This work demonstrates that better results can be achieved if the particular features of each user class are included in the models. It potentially improves the estimation of the responses and allows managers to shape their control measures to address specific user needs.     

On planning dissaturation control signals for urban areas

The problem of the optimal control of an oversaturated intersection, originally proposed by Gazis and Potts (1965), is reviewed and a feedback solution is obtained for a multi-stream pattern under general assumptions on the variability of input flows and control constraints. The dissaturation control problem is solved in two steps: first, the least dissaturation time is evaluated by taking into account the green split constraints; then a minimum waiting time problem over the least dissaturation horizon is solved to select the optimal output rate pattern.     

A basic mathematical model for evacuation problems in urban areas

Real life situations like floods, hurricanes or chemical accidents may cause the evacuation of a certain area to rescue the affected population. To enable a fast and a safe evacuation a basic mixed-integer evacuation model has been developed that provides a reorganization of the traffic routing of a certain area for the case of an evacuation. This basic problem of evacuation minimizes the evacuation-time while prohibiting conflicts within intersections. Our evacuation model is a dynamic network flow problem with additional variables for the number and direction of used lanes and with additional complicating constraints.Because of the size of the time-expanded network, the computational effort required by standard software is already very high for tiny instances. To deal with realistic instances we propose a heuristic approach.     

An optimization method for sustainable traffic control in urban areas

The optimization of traffic signalization in urban areas is formulated as a problem of finding the cycle length, the green times and the offset of traffic signals that minimize an objective function of performance indices. Typical approaches to this optimization problem include the maximization of traffic throughput or the minimization of vehicles’ delays, number of stops, fuel consumption, etc. Dynamic Traffic Assignment (DTA) models are widely used for online and offline applications for efficient deployment of traffic control strategies and the evaluation of traffic management schemes and policies. We propose an optimization method for combining dynamic traffic assignment and network control by minimizing the risk of potential loss induced to travelers by exceeding their budgeted travel time as a result of deployed traffic signal settings, using the Conditional Value-at-Risk model. The proposed methodology can be easily implemented by researchers or practitioners to evaluate their alternative strategies and aid them to choose the alternative with less potential risk. The traffic signal optimization procedure is implemented in TRANSYT-7F and the dynamic propagation and route choice of vehicles is simulated with a mesoscopic dynamic traffic assignment tool (DTALite) with fixed temporal demand and network characteristics. The proposed approach is applied to a reference test network used by many researchers for verification purposes. Numerical experiments provide evidence of the advantages of this optimization method with respect to conventional optimization techniques. The overall benefit to the performance of the network is evaluated with a Conditional Value-at-Risk Analysis where the optimal solution is the one presenting the least risk for ‘guaranteed’ total travel times.     

A new shared vehicle system for urban areas

The paper concerns the conceptual design of a transport system for pedestrian areas. The proposed transport system is based on a fleet of eco-sustainable Personal Intelligent City Accessible Vehicles (PICAVs). The vehicles are shared through the day by different users and the following specific services will be provided: instant access, open ended reservation and one way trips. Referring to the proposed transport system, a new methodology to optimise the fleet dimension and its distribution among the stations is proposed in this paper. The problem faced is an optimisation problem where the cost function to be minimised takes into account both the transport system cost and the user costs that depend on the waiting times. A random search algorithm has been adopted. Given a fleet dimension and its distribution among the stations, the waiting times of the users are assessed by a microscopic simulation. The simulation model tracks the second-by-second activity of each PICAV user, as well as the second-by-second activity of each vehicle. The overall methodology has been implemented in an object-oriented simulator. The proposed transport system has been planned and simulated for the historical city centre of Genoa, Italy.     

Advanced freight transportation systems for congested urban areas

Urban freight transportation constitutes both an extremely important and a rather disturbing activity. Increasingly, one observes efforts to measure and control freight movements within city centers. We introduce a possible organizational and technological framework for the integrated management of urban freight transportation and identify important associated planning and operation issues and models. We then describe a formulation for one of these problems, the design of the proposed logistical structure, and discuss algorithmic and implementation issues. Our model city and challenge is Rome.     

A bikeway network design model for urban areas

This work develops a bikeway network design model for cycling in urban areas. The objectives of the proposed model are to minimize cyclist risk, maximize cyclist comfort, maximize service coverage for residents, and minimize the impact of the bikeway on existing traffic. The proposed model considers the following constraints: bikeway type, monetary budgets, path continuities, and value ranges of decision variables. Grey numbers are employed to deal with parameter uncertainties, and the proposed model is developed as a multi-objective grey 0–1 programming problem. A case study of the Xinyi District, Taipei City, is conducted. The grey one-stage algorithm is applied to solve this problem. In total, six non-dominated alternatives are generated for the case study. In comparing model alternatives with the existing network, this work confirms that the existing biking network prioritizes cyclist comfort. Scenario analyses indicate that maintaining the lower bound of the length percentage of wooded areas at 20–40 % generates better performance than that of the other scenarios, while decreasing the number of traffic accidents only decreases cyclist risk and does not alter the other objectives. The proposed model is the first network design model for bikeways in literature and will assist bikeway planners in developing alternatives for further evaluation and in developing detailed designs efficiently and systematically.     

Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas

Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM, to describe the complex behaviors of both passengers and transportation systems in urban cities. BEAM simulates the driving, parking and charging behaviors of the AEV fleet with range constraints and identifies times and locations of their charging demands. Then, based on BEAM simulation outputs, we adopt a hybrid algorithm to site and size charging stations to satisfy the charging demands subject to quality of service requirements. Based on the proposed framework, we estimate the charging infrastructure demands and calculate the corresponding economics and carbon emission impacts of electrifying a ride-hailing AEV fleet in the San Francisco Bay Area. We also investigate the impacts of various AEV and charging system parameters, e.g., fleet size, vehicle battery capacity and rated power of chargers, on the ride-hailing system’s overall costs.