Optimized Combination Model and Algorithm of Parking Guidance Information Configuration

Parking Guidance and Information (PGI) signs are thought to enable a more efficient use of the available parking stock. Despite the installation of PGI systems in many cities and their operation for a number of years, there is a lack of reliable evidence of the size of the benefits that these systems can achieve. This paper describes the development of driver parking choice models (both during the journey and pre-trip) and the implementation of these models in the existing network traffic simulation model RGCONTRAM. Besides quantifying the effects of the PGI system on both the drivers seeking suitable parking spaces and the parking stock itself, this also enables quantification of the impact of parking choice on the wider network. Factors influencing PGI effectiveness are described and conclusions are drawn that illustrate the potential of PGI to induce the demand to spread more efficiently across the parking stock.

Understanding the demand for access information

Parking Guidance and Information (PGI) systems aim at supporting drivers in finding suitable parking spaces, also by predicting the availability at driver’s Estimated Time of Arrival (ETA), leveraging information about the general parking availability situation. To do these predictions, most of the proposals in the literature dealing with on-street parking need to train a model for each road segment, with significant scalability issues when deploying a city-wide PGI. By investigating a real dataset we found that on-street parking dynamics show a high temporal auto-correlation. In this paper we present a new processing pipeline that exploits these recurring trends to improve the scalability. The proposal includes two steps to reduce both the number of required models and training examples. The effectiveness of the proposed pipeline has been empirically assessed on a real dataset of on-street parking availability from San Francisco (USA). Results show that the proposal is able to provide parking predictions whose accuracy is comparable to state-of-the-art solutions based on one model per road segment, while requiring only a fraction of training costs, thus being more likely scalable to city-wide scenarios.

With the development of the city, the parking trouble is becoming an outstanding social and transport problem. A reasonable parking pricing scheme makes contribution to improve the use efficiency of parking and to reduce traffic congestion. The paper employs the theories and methods of behavior science to analyze parking behavior spatial structure, and establish the parking pricing model based on parking choice behavior. An algorithm based on the method of restricted capacity and incremental assignment was designed. Parking plays an important role in the traffic system of most major cities. With the developing city, the parking trouble is becoming an outstanding social and transport problem, such as motorists have been observed spending a significant percentage of their total trip time searching for a car park space. Within the city, this can deteriorate the status of traffic congestion and environmental quality. At the same time, unreasonable parking pricing will cause an unreasonable usage of the parking lot. Hence, the parking pricing model based on parking choice behavior is being a noteworthy problem in the traffic field. For solved parking problem in city, there are three stages in the developed country (GUAN, 2002). First is increase capacity of car parking lots, so that increase supply of parking for parking demand. Second is control capacity of parking lots in urban center, and control traffic demand. Three is make use of parking policy to adjust traffic structure of the city. In China, the most of city are actively planning and building parking lots to increase parking supply. But this method of resolved parking problem is unfeasible because there are many conditionality ingredients, such as land, capital, and so on. So it is a necessary choice that using policy and control means to achieve basic spatial-temporal equilibrium between supply and demand in the traffic system. For coming true this goal, it is one of possible approaches that made a reasonable parking fee to regulate the demand of parking. Many studiers already brought forward many parking pricing models, but these models cannot reflect the relationship between the parking fee and the parking choice behavior. This paper describes a parking pricing model based on parking behavior, and an algorithm of this model is designed.

Vehicle cruising for a destination-nearby parking lot has been a problem to drivers in cities. This problem causes drivers to consume unexpected time and resources to compete for a parking lot. Conventional parking schemes are in place to predict or reserve the parking spaces. Some of these schemes optimize the total cost only for locating parking spaces and road traffic condition is usually ignored. In this investigation, key road traffic conditions, such as vehicle queueing at the intersection of roads and parking fee information, are considered since they influence time and cost in the parking process. As such, a new comprehensive parking scheme is needed in place to assist drivers to identify the appropriate parking lot, estimate the transportation time to reach the parking lot, as well as the parking expenses. In essence, the total journey time and the total cost are important factors. However, former research lacked a harmonious consideration of these two factors. In this paper, a new parking model (PM) is developed for a holistic optimization of time and cost. The performance is measured by a newly defined indicator, referred to as a figure of merit, which is a fusion of time and cost. The analysis reveals that the incorporation of PM improves the performance by 24% ~43%.

Optimization of Urban Single-line Metro Timetable for Total Passenger Travel Time under Dynamic Passenger Demand

This paper describes a case study of the school bus routing problem. It is formulated as a multi-objective combinatorial optimisation problem. The objectives considered include minimising the total number of buses required, the total travel time spent by pupils at all pick-up points, which is what the school and parents are concerned with most, and the total bus travel time. It also aims at balancing the loads and travel times between buses. A heuristic algorithm for its solution is proposed. The algorithm has been programmed and run efficiently on a PC. Numerical results are reported using test data from a kindergarten in Hong Kong. It has shown to be effective with a saving of 29% in total travelling times when comparing to current practice.

As the ratio of the actual number of parked vehicles to the number of parking spaces, the parking lots saturation degree of new-type service area is not only related to the travel experience of drivers and passengers, but also affects the safety and operation efficiency of new-type service areas. However, the current studies do not directly reflect the operation of vehicles in parking lots at different levels of supply and demand, and are rarely directly applied to the design and construction of new-type service areas. In order to study the effect of parking lot saturation degree on vehicle driving in new-type service areas, and apply the research findings to the planning and construction of new-type service areas, considering the dynamic and real-time characteristics of saturation degree, parking lot models of new-type service areas are established by means of simulation modeling relying on VISSIM traffic simulation platform. Combined with the field research results of newtype service areas, delay time and travel time of vehicles are selected as indicators to reflect the degree of mutual interference between vehicles, and the relationship between saturation degree and mutual interference between vehicles in parking lots is clarified. The results show that the degree of mutual interference between vehicles is gradually significant with the increase of saturation degree, and the more parking spaces share a transfer lane, the more obvious the influence of saturation degree on mutual interference between vehicles. When the saturation degree exceeds 80%, the interference between vehicles becomes serious and some vehicles entering parking lots cannot find parking spaces. It is suggested that the saturation degree should not exceed 80% when determining the scale of new-type service areas. At the same time, in the planning and construction of new-type service areas, it should avoid sharing one transfer lane with too many parking spaces, so as to reduce mutual interference between vehicles.

Traffic data collection and traffic state estimation are essential for traffic congestion management within smart cities framework. To overcome the inherent shortcomings of single-source data collection techniques, this paper investigates a data fusion method, employing fix and floating (or probe) data types, to estimate traffic states on both freeway and signalized arterial links. The proposed method explores the relationships among the number of probe data, sampling interval of probe vehicles, probe vehicles penetration rate, estimation interval, and traffic density. This paper introduces the definition of generalized penetration rate based on the quotient of travel time of probe vehicles and total travel time. Furthermore, traffic generalized density is estimated with probe data and observed long-term average of actual penetration rate of probe vehicles. Furthermore, real-time penetration rate of probe vehicles is estimated with probe data and density measurements collected by fixed loop detectors. The validity of the proposed method is investigated by extensive tests on NGSIM field data and microsimulation data that reveal accurate performance for real-time traffic state estimation. The proposed methods demonstrate promising extensions for network-wide traffic state estimation.

Hazards in Karst and Managing Water Resources Quality

Transportation impact assessment following a potential Alpine fault earthquake in New Zealand

This paper proposes a model to find the optimal location of autonomous vehicle lanes in a transportation network consisting of both Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) while accounting for the roadway capacity variation. The main contribution of the model is considering a generalized definition of capacity as a function of AV proportion on a link and incorporating it into the network design problem. A bilevel optimization model is proposed with total travel time as the objective function to be minimized. At the upper-level problem, the optimal locations of AV lanes are determined, and at the lower level which is a multiclass equilibrium assignment, road users including both AVs and HDVs seek to minimize their individual travel times. It is shown that if capacity variation is ignored, the effect of AV lane deployment can be misleading. Since there will be a long transition period during which both AVs and HDVs will coexist in the network, this model can help the network managers to optimally reallocate the valuable road space and better understand the effects of AV lane deployment at the planning horizon as well as during the transition period. Employing this model as a planning tool presents how the proposed AV lane deployment plan could consider the AV market penetration growth during the transition period. Numerical analysis based on the Sioux Falls network is presented in two cases with and without variable capacity to illustrate the application of this model. At the 60% penetration rate of AVs, the improvement in total travel time was 3.85% with a fix capacity while this improvement was 9.88% with a variable capacity.

The purpose of the present study was to obtain a better understanding of the mode choice behaviour of visitors to Expo 2010 Shanghai and the effectiveness of various traffic management policies to reduce private transport use. Due to the possible range of survey respondents, a two-stage gradual stated preference survey method was used. Based on a large-scale stated preference survey conducted among tourists in an airport, a train station and highway service stations in Shanghai, three versions of a mixed logit model for local and out-of-town visitors were developed. The results suggest that local visitors were more sensitive to travel cost than total travel time, whereas out-of-town visitors were particularly concerned about total travel time. A scenario analysis reveals that establishing a restricted traffic zone, adjusting parking rates and providing priority lanes for Expo buses had different effects on trip mode choice. These policies should thus be implemented in combination to achieve the aim of reducing private transport use to less than 10%. An additional finding is that the Expo shuttle bus is a more attractive public transport mode than the subway among Expo visitors.

Parking information system with artificial neural network

A parking guidance and information system for the Shinjuku area of Tokyo is described as Japan's first parking guidance and information system (PGIS) to have a full-fledged guidance function. The concept of the Shinjuku area parking guidance and information system is outlined. The surveys conducted before and after the introduction of the Shinjuku area PGIS confirmed its effectiveness in: (1) improving the convenience of users; (2) reducing on-street parking; (3) enhancing the order of traffic in the Shinjuku area; (4) absorbing into off-street parking lots those cars parked on-street for a short time; and (5) equalizing the number of cars parked between available parking lots. The measures to be taken for further improving the Shinjuku area PGIS are presented at the end.