Unsafe driving behaviour and four wheel drive vehicles: observational study

Objective To assess the level of compliance with the new law in the United Kingdom mandating penalties for rising a hand held mobile phone while driving, to compare compliance with this law with the one on the use of seat belts, and to compare compliance with these laws between drivers of four wheel drive vehicles and drivers of normal cars. Design Observational study with two phases-one within the grace period, the other starting one week after penalties were imposed on drivers using such telephones. Setting Three busy sites in London. Participants Drivers of 38 182 normal cars and 2944 four wheel drive vehicles. Main outcome measures Proportions of drivers seen to be using hand held mobile phones and not using seat belts. Results Drivers of four wheel drive vehicles were more likely than drivers of other cars to be seen using hand held mobile phones (8.2% v 2.0%) and not complying with the law on seat belts (19.5% v 15.0%). Levels of non-compliance with both laws were slightly higher in the penalty phase of observation, and breaking one law was associated with increased likelihood of breaking the other. Conclusions The level of non-compliance with the law on the use of hand held mobile phones by drivers in London is high, as is non-compliance with the law on seat belts. Drivers of four wheel drive vehicles were four times more likely than drivers of other cars to be seen using hand held mobile phones and slightly more likely not to comply with the law on seat belts.

Improved Squeaky Wheel Optimisation for Driver Scheduling

This paper presents a technique called Improved Squeaky Wheel Optimisation (ISWO) for driver scheduling problems. It improves the original Squeaky Wheel Optimisation’s (SWO) effectiveness and execution speed by incorporating two additional steps of Selection and Mutation which implement evolution within a single solution. In the ISWO, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The Analysis step first computes the fitness of a current solution to identify troublesome components. The Selection step then discards these troublesome components probabilistically by using the fitness measure, and the Mutation step follows to further discard a small number of components at random. After the above steps, an input solution becomes partial and thus the resulting partial solution needs to be repaired. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, the optimisation in the ISWO is achieved by solution disruption, iterative improvement and an iterative constructive repair process performed. Encouraging experimental results are reported.

An evolutionary squeaky wheel optimisation approach to personnel scheduling

The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyse a technique called Evolutionary Squeaky Wheel Optimisation and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimisation improves the original Squeaky Wheel Optimisation’s effectiveness and execution speed by incorporating two additional steps (Selection and Mutation) for added evolution. In the Evolutionary Squeaky Wheel Optimisation, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The aim of the Analysis step is to identify below average solution components by calculating a fitness value for all components. The Selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The Mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, improvements in the Evolutionary Squeaky Wheel Optimisation is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.