Modelling behavioural rules for daily activity scheduling using fuzzy logic

The paper focuses on how trip time variability affects re-scheduling of daily activities. A delay in a trip or an early arrival can contribute to changes in the timing, location of the next activity, and to the deletion/addition of some activities. We propose the idea of using fuzzy logic rules to explain the effect of variability in travel time on the benefits perceived by an individual with the changes, and to model different actions that the individuals take in order to re-establish the steadiness of the existing timetable. The fuzzy model is used to handle the imprecision of the data which is unstructured text. The results show that large deviations in trip duration are more likely to induce significant changes in the timetable whereas small deviations are either ignored or translated into modified timing of the next activity. In choosing an action, greater importance is assigned to the flexibility of the following activity, to the magnitude of the trip time saving/delay, and to the duration of the next activity. Time savings are not favoured unless they can be readily transferred into additional activity time allocated to the next activity or to a new activity. The fuzzy rules based system is capable of predicting satisfactorily the strategy of coping with uncertainty in travel times and the satisfaction sensed with the change.     

A genetic algorithm‐optimized fuzzy logic controller to avoid rear‐end collisions

In this paper, a rear‐end collision control model is proposed using the fuzzy logic control scheme. Through detailed analysis of car‐following cases, our fuzzy control system is established with reasonable control rules. Furthermore, a genetic algorithm is introduced into the fuzzy rules refining process to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our genetic algorithm‐optimized fuzzy logic controller outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency. Copyright © 2016 John Wiley & Sons, Ltd.     

Individual trip rate transferability analysis based on a decision tree approach

Transferring trip rates to areas without local survey data is a common practice which is typically performed in an ad hoc fashion using household-based cross-classification tables. This paper applies a rule-based decision tree method to develop individual-level trip generation models for eight different trip purposes as defined in the US National Household Travel Survey in addition to daily vehicle miles traveled. For each trip purpose, the models are obtained by finding the best fitted statistical distribution to each of the final decision tree clusters while considering the correlation between the trip rates for other trip purposes. The rule-based models are sensitive to changes in demographics. The performance of the models is then tested and validated in a transferability application to the Phoenix Metropolitan Region. These models can be employed in a disaggregate microsimulation framework to generate trips with different purposes at the individual or household level.     

并联混合动力汽车改进模糊控制策略研究

本文提出一种兼顾电池SOC限值方法的混合动力汽车多种群遗传模糊控制策略。引入模糊逻辑控制以增强整车控制系统鲁棒性、实时性;用多种群遗传算法对模糊变量隶属度函数进行优化,使在模糊逻辑控制下整车燃油消耗得到降低;使用电池SOC限值方法避免电池在SOC过低时继续放电。利用matlab平台联合advisor软件进行联合仿真实验,仿真结果表明多种群遗传模糊模糊控制策略能够比advisor软件默认的电机辅住控制策略燃油经济性提高6.96%的情况,SOC限值方法使电池工作在更加合理的SOC值区间范围内,有效保护电池。     

A fuzzy logic‐based variable speed limit controller

Variable speed limit (VSL) is an emerging intelligent transportation system (ITS) measure to improve operational and safety performance of motorway systems. Rule‐based algorithms have been widely used in VSL applications because of their comprehensibility and ease of application. However, most of the algorithms proposed in the literature under this category are rather rough for the speed control. Pre‐specified rules show some difficulties in appropriately activating/deactivating control actions in real time because of non‐stationary and nonlinear nature of the traffic system. This paper proposes a fuzzy logic‐based VSL control algorithm as an alternative to the existing VSL control algorithms. The proposed algorithm uses fuzzy sets instead of crisp sets to allow the separation of attribute domains into several overlapping intervals. The discretization using fuzzy sets can help to overcome the sensitivity problem caused by crisp discretization used in the existing VSL algorithms. The proposed algorithm is assessed for a test bed in Auckland using AIMSUN micro‐simulator and verified against a well‐known VSL algorithm. The simulation results show that the proposed algorithm outperforms the existing one to improve the efficiency performance of the motorway system with the critical bottleneck capacity increased by 6.42% and total travel time reduced by 12.39% when compared to a no‐control scenario. Copyright © 2015 John Wiley & Sons, Ltd.     

Household travel attributes transferability analysis: application of a hierarchical rule based approach

This paper presents a system of hierarchical rule-based models of trip generation and modal split. Travel attributes, like trip counts for different transportation modes and commute distance, are among the modeled variables. The proposed framework could be considered as an alternative for several modules of the traditional travel demand modeling approach, while providing travel attributes at the highly disaggregate level that can be also used in activity-based micro-simulation modeling systems. Nonetheless, the modeling framework of this study is not considered as a substitute for activity-based models. The explanatory variables set ranges from socio-economic and demographic attributes of the household to the built environment characteristics of the household residential location. Another important contribution of the study is a framework in which travel attributes are modeled in conjunction with each other and the interdependencies among them are postulated through a hierarchical system of models. All the models are developed using rule-based decision tree method. Moreover, the models developed in this study present a useful improvement in increasing the practicality and accuracy of the rule-based travel data simulation models.     

A new fuzzy approach to estimate the O–D matrix from link volumes

Abstract

Estimation of the origin–destination (O–D) trip demand matrix plays a key role in travel analysis and transportation planning and operations. Many researchers have developed different O–D matrix estimation methods using traffic counts, which allow simple data collection as opposed to the costly traditional direct estimation methods based on home and roadside interviews.

In this paper, we present a new fuzzy model to estimate the O–D matrix from traffic counts. Since link data only represent a snapshot situation, resulting in inconsistency of data and poor quality of the estimated O–Ds, the proposed method considers the link data as a fuzzy number that varies within a certain bandwidth. Shafahi and Ramezani's fuzzy assignment method is improved upon and used to assign the estimated O–D matrix, which causes the assigned volumes to be fuzzy numbers similar to what is proposed for observed link counts. The shortest path algorithm of the proposed method is similar to the Floyd–Warshall algorithm, and we call it the Fuzzy Floyd–Warshall Algorithm. A new fuzzy comparing index is proposed by improving the fuzzy comparison method developed by Dubois and Prade to estimate and compare the distance between the assigned and observed link volumes. The O–D estimation model is formulated as a convex minimization problem based on the proposed fuzzy index to minimize the fuzzy distance between the observed and assigned link volumes. A gradient-based method is used to solve the problem. To ensure the original O–D matrix does not change more than necessary during the iterations, a fuzzy rule-based approach is proposed to control the matrix changes.     

Evaluating competitiveness using fuzzy analytic hierarchy process—A case study of Chinese airlines

With the development of a national market economy, the Chinese aviation industry is now confronted with international competition. Therefore, it is necessary to research the competitive status of Chinese national aviation, as well as advice on how to enhance the competitiveness of the Chinese aviation industry. The main objective of this paper is to propose FAHP as an effective solution for resolving the uncertainty and imprecision in the evaluation of airlines' competitiveness. In this paper, we review the research of industrial international aviation competitiveness at both home and abroad, discuss a theoretical framework for the study of aviation competitiveness, establish an index system with five first‐order indicators and 17 second‐order indicators, set up a Chinese aviation competitiveness model based on simple fuzzy numbers from the fuzzy analytic hierarchy process, and evaluate the competitiveness of five major Chinese airlines. The results showed that this model and these indicators are scientific and practical, with a wide range of application prospects for the purpose of improving and increasing Chinese airline competitiveness in the international market. The effective approach presented in this paper is especially applicable when subjective judgments on performance ratings and attribute weights are not accessible or reliable, or when suitable decision makers are not available. Copyright © 2011 John Wiley & Sons, Ltd.     

The treatment of uncertainty in the dynamic origin–destination estimation problem using a fuzzy approach

Regardless of existing types of transportation and traffic model and their applications, the essential input to these models is travel demand, which is usually described using origin–destination (OD) matrices. Due to the high cost and time required for the direct development of such matrices, they are sometimes estimated indirectly from traffic measurements recorded from the transportation network. Based on an assumed demand profile, OD estimation problems can be categorized into static or dynamic groups. Dynamic OD demand provides valuable information on the within-day fluctuation of traffic, which can be employed to analyse congestion dissipation. In addition, OD estimates are essential inputs to dynamic traffic assignment (DTA) models. This study presents a fuzzy approach to dynamic OD estimation problems. The problems are approached using a two-level model in which demand is estimated in the upper level and the lower level performs DTA via traffic simulation. Using fuzzy rules and the fuzzy C-Mean clustering approach, the proposed method treats uncertainty in historical OD demand and observed link counts. The approach employs expert knowledge to model fitted link counts and to set boundaries for the optimization problem by defining functions in the fuzzification process. The same operation is performed on the simulation outputs, and the entire process enables different types of optimization algorithm to be employed. The Box-complex method is utilized as an optimization algorithm in the implementation of the approach. Empirical case studies are performed on two networks to evaluate the validity and accuracy of the approach. The study results for a synthetic network and a real network demonstrate the robust performance of the proposed method even when using low-quality historical demand data.     

An integrated multi‐objective model to determine the optimal rescue path and traffic controlled arcs for disaster relief operations under uncertainty environments

This study proposes an integrated multi‐objective model to determine the optimal rescue path and traffic controlled arcs for disaster relief operations under uncertainty environments. The model consists of three sub‐models: rescue shortest path model, post‐disaster traffic assignment model, and traffic controlled arcs selection model to minimize four objectives: travel time of rescue path, total detour travel time, number of unconnected trips of non‐victims, and number of police officers required. Since these sub‐models are inter‐related with each other, they are solved simultaneously. This study employs genetic algorithms incorporated with traffic assignment and K‐shortest path methods to determine optimal rescue path and controlled arcs. To cope with uncertain information associated with the damaged network, fuzzy system reliability theory (weakest t‐norm method) is used to measure the access reliability of rescue path. To investigate the validity and applicability of the proposed model, studies on an exemplified case and a field case of Chi‐Chi earthquake in Taiwan are conducted. The performances of three rescue strategies: without traffic control, selective traffic control (i.e. the proposed model) and absolute traffic control are compared. The results show that the proposed model can maintain the efficiency of rescue activity with minimal impact to ordinary trips and number of police officers required.     

A fuzzy logic-genetic algorithm approach to modelling public transport users’ risk-taking behaviour

This paper seeks to determine the effects of uncertainty in out-of-vehicle times on route choice. Data were collected at two key interchanges in Auckland, New Zealand. Previous work modelled the data using a manual approach to fuzzy logic. This study extends that work by automating the process through defining a black-box function to match the survey data, then employing a genetic algorithm to fine-tune the fuzzy logic model. Results show that automation and the genetic algorithm improve the model’s capability to more accurately predict ridership. The tuning of the membership functions is conducted twice, first using initial fuzzy rules and again after the fuzzy rules have been adjusted to reduce disparity between the output and survey data. The calibrated membership functions provided for operational (transfer waiting and walking time and delay) and physical attributes (safety and seat availability) can be used by practitioners to determine an estimated ridership.     

Hybrid simulated annealing and genetic algorithm for optimizing arterial signal timings under oversaturated traffic conditions

The implementation of system‐wide signal optimization models requires efficient solution algorithms that can quickly generate optimal or near‐optimal signal timings. This paper presents a hybrid algorithm based on simulated annealing (SA) and a genetic algorithm (GA) for arterial signal timing optimization. A decoding scheme is proposed that exploits our prior expectations about efficient solutions, namely, that the optimal green time distribution should reflect the proportion of the critical lane volumes of each phase. An SA algorithm, a GA algorithm and a hybrid SA‐GA algorithm are developed here using the proposed decoding scheme. These algorithms can be adapted to a wide range of signal optimization models and are especially suitable for those optimizing phase sequences with oversaturated intersections. To comparatively evaluate the performance of the proposed algorithms, we apply them to a signal optimization model for oversaturated arterial intersections based on an enhanced cell transmission model. The numerical results indicate that the SA‐GA algorithm outperforms both SA and GA in terms of solution quality and convergence rate. Copyright © 2014 John Wiley & Sons, Ltd.     

Modelling users’ behaviour in inter-urban carsharing program: A stated preference approach

In this paper, the effects of a inter-urban carsharing program on users’ mode choice behaviour were investigated and modelled through specification, calibration and validation of different modelling approaches founded on the behavioural paradigm of the random utility theory. To this end, switching models conditional on the usually chosen transport mode, unconditional switching models and holding models were investigated and compared. The aim was threefold: (i) to analyse the feasibility of a inter-urban carsharing program; (ii) to investigate the main determinants of the choice behaviour; (iii) to compare different approaches (switching vs. holding; conditional vs. unconditional); (iv) to investigate different modelling solutions within the random utility framework (homoscedastic, heteroscedastic and cross-correlated closed-form solutions). The set of models was calibrated on a stated preferences survey carried out on users commuting within the metropolitan area of Salerno, in particular with regard to the home-to-work trips from/to Salerno (the capital city of the Salerno province) to/from the three main municipalities belonging to the metropolitan area of Salerno. All of the involved municipalities significantly interact each other, the average trip length is about 30 km a day and all are served by public transport. The proposed carsharing program was a one-way service, working alongside public transport, with the possibility of sharing the same car among different users, with free parking slots and free access to the existent restricted traffic areas. Results indicated that the inter-urban carsharing service may be a substitute of the car transport mode, but also it could be a complementary alternative to the transit system in those time periods in which the service is not guaranteed or efficient. Estimation results highlighted that the conditional switching approach is the most effective one, whereas travel monetary cost, access time to carsharing parking slots, gender, age, trip frequency, car availability and the type of trip (home-based) were the most significant attributes. Elasticity results showed that access time to the parking slots predominantly influences choice probability for bus and carpool users; change in carsharing travel costs mainly affects carpool users; change in travel costs of the usually chosen transport mode mainly affects car and carpool users.     

Ridership estimation for short-range transit planning

The objective of this research was to develop a simple transit ridership estimation model system for short-range planning. The main feature of the model system is that it exploits knowledge of transit link volumes which are obtained readily from on-off counts. Extensive use is made of default values for model parameters, taken directly from the transportation literature. The remaining parameters can be derived easily from generally available land-use and socioeconomic data. Expensive household surveys and time-consuming model calibrations are not required. A sequence of simple trip generation, trip distribution and modal split models generate trip-purpose specific transit trip tables, denoted as “trial” trip tables. These trip tables and observed transit link volumes are used in a linear programming model which serves as a correction mechanism. The gain in accuracy is achieved by using the ridership information contained in the transit link volumes. The corrected trip tables may be used in a pivot-point analysis to estimate changes in ridership and revenue. The results of a test application of the model system indicate that it can generate accurate ridership estimates when reliable transit link volumes are available from on-off counts, and when the trial transit trip tables as derived from the first three component models are reasonably accurate.     

A note on the calculation and calibration of doubly constrained trip distribution models

The use of growth factor models for trip distribution has given way in the past to the use of more complex synthetic models. Nevertheless growth factor models are still used, for example in modelling external trips, in small area studies, in input-output analysis, and in category analysis. In this article a particular growth factor model, the Furness, is examined. Its application and functional form are described together with the method of iteration used in its operation. The expected information statistic is described and interpreted and it is shown that the Furness model predicts a trip distribution which, when compared with observed trips, has the minimum expected information subject to origin and destination constraints. An equivalent entropy maximising derivation is described and the two methods compared to show how the Furness iteration can be used in gravity models with specified deterrence functions. A trip distribution model explicitly incorporating information from observed trips, is then derived.It is suggested that if consistency is to be maintained between iteration, calibration, and the derivation of gravity models, then expected information should be used as the calibration statistic to measure goodness of fit. The importance of consistency in this respect is often overlooked.Lastly, the limitations of the models are discussed and it is suggested that it may be better to use the Furness iteration rather than any other, since it is more fully understood. In particular its ease of calculation makes it suitable for use in small models computed by hand.     

Real-time trip purpose prediction using online location-based search and discovery services

The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.     

Rationalizing Reliable Imputation Durations of Genetically Designed Time Delay Neural Network and Locally Weighted Regression Models

Abstract

Estimating missing values is known as data imputation. Previous research has shown that genetic algorithms (GAs) designed locally weighted regression (LWR) and time delay neural network (TDNN) models can generate more accurate hourly volume imputations for a period of 12 successive hours than traditional methods used by highway agencies. It would be interesting and important to further refine the models for imputing larger missing intervals. Therefore, a large number of genetically designed LWR and TDNN models are developed in this study and used to impute up to a week-long missing interval (168 hours) for sample traffic counts obtained from various groups of roads in Alberta, Canada. It is found that road type and functional class have considerable influences on reliable imputations. The reliable imputation durations range from 4–5 days for traffic counts with most unstable patterns to over 10 days for those with most stable patterns. The study results clearly show that calibrated GA-designed models can provide reliable imputations for missing data with ‘block patterns’, and demonstrate their further potentials in traffic data programs.     

Integrated fuzzy signal and ramp‐metering at a diamond interchange

We propose a fuzzy logic control for the integrated signal operation of a diamond interchange and its ramp meter, to improve traffic flows on surface streets and motorway. This fuzzy logic diamond interchange (FLDI) comprises of three modules: fuzzy phase timing (FPT) module that controls the green time extension of the current phase, phase logic selection (PLS) module that decides the next phase based on the pre‐defined phase sequence or phase logic and, fuzzy ramp‐metering (FRM) module that determines the cycle time of the ramp meter based on current traffic volumes and conditions of the surface streets and the motorways. The FLDI is implemented in Advanced Interactive Microscopic Simulator for Urban and Non‐Urban Network Version 6 (AIMSUN 6), and compared with the traffic actuated signal control. Simulation results show that the FLDI outperforms the traffic‐actuated models with lower system total travel time, average delay, and improvements in downstream average speed and average delay. Copyright © 2011 John Wiley & Sons, Ltd.     

Geographic information system design for network equilibrium-based travel demand models

Travel demand analyses are useful for transportation planning and policy development in a study area. However, travel demand modeling faces two obstacles. First, standard practice solves the four travel components (trip generation, trip distribution, modal split and network assignment) in a sequential manner. This can result in inconsistencies and non-convergence. Second, the data required are often complex and difficult to manage. Recent advances in formal methods for network equilibrium-based travel demand modeling and computational platforms for spatial data handling can overcome these obstacles. In this paper we report on the development of a prototype geographic information system (GIS) design to support network equilibrium-based travel demand models. The GIS design has several key features, including: (i) realistic representation of the multimodal transportation network, (ii) increased likelihood of database integrity after updates, (iii) effective user interfaces, and (iv) efficient implementation of network equilibrium solution algorithms.